Fountain, Anne O.

B.A. With Honors in Spanish, Indiana University, 1966 M.A. in Spanish, Indiana University, 1968 Ph.D. in Spanish, Columbia University, 1973 Graduate Area Certificate, Latin American Studies, Columbia University, School of International Affairs, 1973https://scholarworks.sjsu.edu/erfa_bios/1035/thumbnail.jp

Multiclass lung disease classification in chest X-ray images: A fine-tuned hybrid CNN-GNN approach using transfer learning and feature extraction

In clinical practice, it is still difficult to accurately diagnose lung diseases from chest X-ray (CXR) images. In this study, we propose a new hybrid method for identifying several types of lung diseases using CXR images by combining Convolutional Neural Networks (CNNs) with Graph Neural Networks (GNNs). The framework of our proposed methodology takes advantage of CNN’s ability to extract detailed visual features and GNN’s capacity to understand complex relationships between these features, enabling comprehensive analysis in a multi-class classification setting of COVID-19, pneumonia, and normal lung conditions. We used multiple transfer learning models such as DenseNet201, VGG16, VGG19, MobileNetV2, and ResNet50 as backbones for extracting rich feature representations from the CXR images. We used K-Means clustering on the extracted CNN feature vectors to transform them into a graph structure in which clusters were nodes with edges representing similarity based on cluster association for images sharing similar pathological features. Instead of selecting a single CNN model, we experimented with various CNN-GNN hybrid combinations to capture local visual characteristics as well as global relational patterns. As a part of our GNN model, we have developed a Graph Convolutional Network (GCN) hybridized with the Graph Attention Network (GAT). This GCN-GAT hybrid model used the graph structure to uncover complex patterns by analyzing the connections between features to perform lung disease classification. Our best performing hybrid CNN-GNN model delivered impressive performance metrics, with an accuracy of 98.53%, an F1 score of 98.52%, an ROC-AUC of 99.81%, a recall of 98.53%, and a specificity of 99.26% in the multi-class classification task

Reinforcement Learning-based Dynamic Pricing for Revenue Maximization with Elastic Network Slicing

Network slicing is a key enabler of next-generation networking that supports a diverse array of network applications with different service requirements. In particular, elastic network slicing that dynamically scales the bandwidth reserved for each network slice would benefit both slice users and providers through cost-effective resource utilization. However, the elasticity poses a complex problem of managing the dynamics of fluctuating network slices. It is necessary for a slice provider to maintain the balance of different types of slice requests, so it can accommodate more requests while satisfying the service requirements for each slice type. Dynamic pricing of slice resources is a way for a network operator to realize the ideal balance of different types of network slices by implicitly communicating the current network state to slice users. In this project, we formulate an online pricing scheme for elastic network slices, which maximizes the revenue of slice providers. Our problem considers (1) slice users’ a priori preference over different types of network slices (susceptibility to Service Level Agreement violations) and (2) the influence of prices on slice users’ decision to choose a type of slice services. Our simulation experiments in a practical network topology demonstrate the revenue increase of a network operator by encouraging the use of elastic network slices

Spartan Daily, April 30, 2024

Volume 162, Issue 38https://scholarworks.sjsu.edu/spartan_daily_2024/1038/thumbnail.jp

Ensemble Model with Meta-Learning for DDoS Attack Classification in SDN

In response to the security threats posed by Distributed Denial of Service (DDoS) attacks, this paper presents an intrusion detection framework with a high-accuracy multi-class classification model. In addition to detecting the existence of DDoS attacks, our framework aims to identify the type of attack (e.g., protocol or message type) so that the system can select the most appropriate countermeasure against the DDoS type. We leverage a meta-learner to build an ensemble model of multiple machine learning models such as LSTM, RF, and KNN to enhance detection and classification accuracy. Tested on the CIC-DDoS 2019 dataset, the proposed model achieves 96% accuracy in the type identification task, while a simple combination of existing detection classifiers only achieves 92% accuracy in the same type identification. To demonstrate a practical implementation of the DDoS classification model, we integrated the model with the Ryu SDN controller running on a Mininet network testbed, which emulates different types of DDoS attacks and benign traffic. The integrated ensemble model achieved 93% accuracy in identifying the DDoS types in the testbed experiment

Spartan Daily, March 19, 2024

Volume 162, Issue 23https://scholarworks.sjsu.edu/spartan_daily_2024/1023/thumbnail.jp

Setting research priorities for global pandemic preparedness: An international consensus and comparison with ChatGPT’s output

Background In this priority-setting exercise, we sought to identify leading research priorities needed for strengthening future pandemic preparedness and response across countries. Methods The International Society of Global Health (ISoGH) used the Child Health and Nutrition Research Initiative (CHNRI) method to identify research priorities for future pandemic preparedness. Eighty experts in global health, translational and clinical research identified 163 research ideas, of which 42 experts then scored based on five pre-defined criteria. We calculated intermediate criterion-specific scores and overall research priority scores from the mean of individual scores for each research idea. We used a bootstrap (n = 1000) to compute the 95% confidence intervals. Results Key priorities included strengthening health systems, rapid vaccine and treatment production, improving international cooperation, and enhancing surveillance efficiency. Other priorities included learning from the coronavirus disease 2019 (COVID-19) pandemic, managing supply chains, identifying planning gaps, and promoting equitable interventions. We compared this CHNRI-based outcome with the 14 research priorities generated and ranked by ChatGPT, encountering both striking similarities and clear differences. Conclusions Priority setting processes based on human crowdsourcing – such as the CHNRI method – and the output provided by ChatGPT are both valuable, as they complement and strengthen each other. The priorities identified by ChatGPT were more grounded in theory, while those identified by CHNRI were guided by recent practical experiences. Addressing these priorities, along with improvements in health planning, equitable community-based interventions, and the capacity of primary health care, is vital for better pandemic preparedness and response in many settings

Hill, Patricia Evridge

University of Texas at Dallas, Humanities, Emphasis in History, Ph.D. 1990 University of Texas at Dallas, Humanities, Emphasis in History, M.A. 1984 Southern Methodist University, History Major; Spanish Minor, B.A. 1979 Lifetime Texas Teaching Credential Grades 6 through 12, History and Spanish, 1980https://scholarworks.sjsu.edu/erfa_bios/1374/thumbnail.jp