SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
Browse
3 results
Search Results
Item Restricted Deep Learning Approaches for Multivariate Time Series: Advances in Feature Selection, Classification, and Forecasting(New Mexico State University, 2024) Alshammari, Khaznah Raghyan; Tran, Son; Hamdi, Shah MuhammadIn this work, we present the latest developments and advancements in the machine learning-based prediction and feature selection of multivariate time series (MVTS) data. MVTS data, which involves multiple interrelated time series, presents significant challenges due to its high dimensionality, complex temporal dependencies, and inter-variable relationships. These challenges are critical in domains such as space weather prediction, environmental monitoring, healthcare, sensor networks, and finance. Our research addresses these challenges by developing and implementing advanced machine-learning algorithms specifically designed for MVTS data. We introduce innovative methodologies that focus on three key areas: feature selection, classification, and forecasting. Our contributions include the development of deep learning models, such as Long Short-Term Memory (LSTM) networks and Transformer-based architectures, which are optimized to capture and model complex temporal and inter-parameter dependencies in MVTS data. Additionally, we propose a novel feature selection framework that gradually identifies the most relevant variables, enhancing model interpretability and predictive accuracy. Through extensive experimentation and validation, we demonstrate the superior performance of our approaches compared to existing methods. The results highlight the practical applicability of our solutions, providing valuable tools and insights for researchers and practitioners working with high-dimensional time series data. This work advances the state of the art in MVTS analysis, offering robust methodologies that address both theoretical and practical challenges in this field.14 0Item Restricted DEEP LEARNING APPROACH TO LARGE-SCALE SYSTEMS(2023) Altamimi, Abdulelah; Lagoa, ConstantinoThe significance of large-scale systems has increased recently due to the growth in data and the number of users. The computational cost of analyzing these high-dimensional systems due to the curse of dimensionality raises the urge for developing efficient approaches. Deep learning methods have the capability and scalability to process high-volume data with significantly lower computational complexity. In this work, deep learning algorithms are utilized to solve large-scale systems in different applications. We design and solve high-dimensional systems using tractable algorithms. In particular, the deep reinforcement learning method and deep neural network are employed in our work in maximizing problems and classification problems, respectively. Comparisons with conventional algorithms are performed for validation purposes. Moreover, this work proposes an approach to exploiting the knowledge of the physical structure of plants inspired by deep learning algorithms. An application in the forest management field considered in this work is a large-scale forest model for wildfire mitigation. A high-dimensional forest model is designed in the Markov decision process framework. The model includes the probability of wildfire occurrence in a large number of stands. The probability of wildfire in each stand is a function of wind direction, flammability, and the stand's timber volume. Wildfire reduction is achieved by maximizing the timber volume in the forest through management actions. A deep reinforcement learning approach, i.e., the actor-critic algorithm, is used to solve the Markov decision process and propose management policies. Furthermore, the performances of conventional Markov decision process solutions, i.e., the value iteration algorithm and the genetic algorithm, are compared to the proposed approach. It outperforms these algorithms in terms of the value of the timber volume and the computational cost. Another interesting application considered in this thesis is fast stochastic predictive control. In the proposed approach, the computational complexity of solving stochastic predictive control is significantly reduced using deep learning. In particular, the number of constraints in the sampled method is reduced to the minimal set required to solve the optimization problem. Determining these constraints,i.e., the policies, is considered a classification problem to be solved using a neural network. The small number of constraints and the solvable quadratic optimization problem introduced by the sampled method result in a fast stochastic model predictive control. In this thesis, we also propose an approach to exploiting the prior knowledge of the physically interconnected systems in the parameter estimation domain. Unlike the physics-informed neural network, the proposed approach can estimate the parameters for every system in the interconnection. It has a general form that can be applied to any system as well as an objective function. We also combine the case of prior knowledge of system function with the case of the unavailability of this information. The Fourier series approximation method is used when knowledge of system functions is not available. The first-order gradient descent algorithm is considered to minimize the estimation error in the objective function. For that, we provide a systematic way to compute the gradients of the objective function. Using several versions of the gradient descent algorithm, the proposed solution shows promising results in the estimation of the system parameters.33 0Item Restricted Using Deep Learning Techniques for an Early Detection of Oral Epithelial Dysplasia(2023) Aljuaid, Abeer; Anwar, MohdOral cancer is ranked as the sixth most common type of cancer worldwide, with 90% of cases being oral squamous cell carcinoma (OSCC). OSCC has a high mortality rate, and early diagnosis can increase the survival rate. About 80% of OSCC is developed from Oral Epithelial Dysplasia (OED); thus, OED detection is critical to diagnose OSCC at the early stage. Traditionally, the OED is defined by sixteen criteria, including architectural and cytological features, under the microscope by expert oral pathologists. This manual detection is a time-consuming and tedious task, and thus, there is a need for precise automated diagnostic and classification techniques. However, disengaging a Computer Aided Diagnosis (CAD) for OED is challenging because each OED’s criteria require a particular medical image processing task for detection. Therefore, we proposed a novel multi-task learning network to combine semantic segmentation and classification to detect OED using architectural and cytological characteristics. Our proposal is the first study that jointly trained semantic segmentation and classification on a single network for automated OED detection. We developed four new frameworks called VGG16-UNet, InceptionV3-UNet, DyspVGG16, and Dysp-InceptionV3. The VGG16-UNet and InceptionV3-UNet were designed based on classic U-Net with the ImageNet pre-trained VGG16 and InceptionV3 encoder and a traditional classifier model. We built Dysp-VGG16 and Dysp-InceptionV3 using our novel modified U-Net and novel classifier network. Our modified U-Net involved dilated convolution, channel attention, spatial attention, and residual blocks for performance enhancement. The proposed models’ effectiveness and robustness were verified by running three experiments and utilizing quantitative metrics and visualization results for comparison. Consequently, our novel modified U-Net and classifier network show superior performance on classification and segmentation tasks. Our novel classifier enhanced the quantitative metrics and reduced the traditional classifier’s false positives and negative rates. Modified U-Net improved the semantic segmentation performance by 5% of the Jaccard index and provided accurate predicted masks.28 0