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Item Restricted Machine Learning Classififiers for Chronic Obstructive Pulmonary Disease Assessment Using Lung CT Data.(Western University, 2024-04-12) Alsurayhi, Halimah; Abbas, SamaniChronic Obstructive Pulmonary Disease (COPD) is a condition characterized by persistent inflammation and airflow blockages in the lungs, contributing to a significant number of deaths globally each year. To guide tailored treatment strategies and mitigate future risks, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) employs a multifaceted assessment system of COPD severity, considering patient's lung function, symptoms, and exacerbation history. COPD staging systems, such as the high-resolution eight-stage COPD system and the GOLD 2023 three staging systems, have been later developed based on these factors. Lung Computed Tomography (CT) is becoming increasingly crucial in investigating COPD as it can detect various COPD phenotypes, such as emphysema, bronchial wall thickening, and gas trapping. Deep learning techniques show promise in leveraging CT imaging to assess the severity of COPD. This thesis uses lung CT data in conjunction with machine learning techniques to classify COPD patients according to these staging systems. For the eight-stage system, both Neural Network and Convolutional Neural Network (CNN) approaches were employed for classification. To develop the Neural Network model, features were extracted from lung CT scans at inspiration and expiration breathing phases, including lung air features and COPD phenotypes features. The CNN model utilized a single lung CT scan at the expiration phase. The GOLD 2023 three staging system involves training separate CNN models using lung CT scans at expiration to predict symptom levels and COPD exacerbation risk. In this thesis, in addition to models trained from scratch, Transfer Learning was also employed to develop models for the eight-stage COPD classification, Symptom level prediction, and exacerbation risk prediction. The developed classifiers demonstrate reasonably high classification performance, indicating their potential for deployment in clinical settings to enhance COPD assessment using image data.13 0Item Restricted Logic-Oriented Fuzzy Neural Networks: Optimization and Applications of Interpretable Models of Machine Learning(Saudi Digital Library, 2023-10-02) Alateeq, Majed Mohammad; Pedrycz, WitoldWith the rapid development of machine learning models along with increasingly complex data structures, it becomes difficult to ground the reliability of models’ predictions despite the substantial progress in favor of high approximation properties. The lack of interpretability remains a key barrier in order to fully leverage the tremendous success of intelligent systems since it delivers critical analysis abilities to the end user to achieve efficiency in decision-making processes. The purpose of Interpretability and transparency is to reveal interconnections of intelligent models leading to justifying decision-making process, eliminating vagueness and capturing a factor of uncertainty in data space. Therefore, any advancement made to the interpretability feature will positively impact overall models’ performance. In the presented considerations, this work relies on logic-oriented fuzzy neural networks to represent knowledge in a transparent way with the aid of information granules. In the synergistic collaboration with fuzzy logic, neural networks deliver a vast array of learning abilities that can be even augmented with various fuzzy analytical methods to discover hidden data patterns for better interpretability. The high modularity of the constructed networks (leading to multifunctionality and robustness) is inherited from the logic nature of AND/OR neurons. The logic-oriented neurons play a pivotal role in the developed models and realize a logic approximation of experimental data and reflect general decomposition of Boolean function in two-valued logic. Information granularity is a key component in building abstract concepts to humans for knowledge acquisition and reasoning. In fact, information granules serve as a vehicle to interpret and represent knowledge domain, offering efficient way to describe complex and nonlinear systems. Fuzzy sets, as a form of information granules, adequately handle imprecise and vague knowledge in systems and consequently are a key in building transparent and interpretable models. Thus, humans can easily comprehend real-world systems or natural phenomena. The overall model efficiency, expressed in terms of accuracy and interpretability when dealing with the design and validation of AND/OR networks, constitutes a focal point of this research, along with effective quantification of the extracted knowledge especially in the case of high-dimensional input–output space. The primary objective of this dissertation is to analyze and design a cohesive interpretable framework capable of maintaining high approximation capabilities. In this study, we used logic-oriented fuzzy AND\OR networks as a backbone of overall interpretable framework. Starting off with structural analysis of the network, the structure exhibits low efficiency caused by gradient-based learning algorithms. Therefore, other gradient-based learning alternatives are superior in improving convergence due to their adaptive learning mechanisms. We demonstrate that the rate of convergence can be improved significantly by integrating randomized learning techniques through generating random weight values of connectives. Furthermore, we proposed an innovative interpretable method to describe and quantify data using concepts. The approach describes reference information granules positioned in some space (output space) inducing fuzzy sets localized in the input space. The description is realized by running a conditional fuzzy clustering followed by a calibration process completed through logic networks. The synergy between conditional clustering and logic networks presents highly cohesive linguistic dependency between objects and their attributes. As for the interpretability, a thoroughly discussion of interpretation aspects of concept analysis and conceptual clustering is presented as a means for uncertainty quantification and rigorous explainability. Further enhancement of the interpretation framework is proposed by presenting a novel method of conditional clustering. We developed a mathematical model that takes into consideration multi conditions positioned in the output space to induce information granules in input space simultaneously making these types of models more reflective of reality. The experimental studies involve synthetic data machine learning datasets from publicly-available repositories.5 0Item Restricted A Tool For Indexing And Classifying Unstructured Textual Documents Based on Product Family Algebra(2020-08-01) Alomair, Deemah; Khedri, RidhaUnstructured textual documents comprise the bulk of the data used and archived by organizations within all sectors of the economy. The need to index and classify these documents became an interesting topic that gained more attention in the field of data analytic. Different approaches are used to perform indexing and classification of textual documents. They range from supervised Machine Learning (ML) approaches to rule-based ones. There is a need for exploring novel classification approaches that exhibit better effectiveness and performance in classifying the increasing volume of this kind of data. In this thesis, we propose a novel approach to index and classify unstructured textual documents based on Product Family Algebra (PFA) and implemented using Binary Decision Diagram (BDD). In the proposed approach, a signature is first constructed for a document or a family of documents. The signature is relative to a dictionary of the typical words used in the category under consideration. Then, using operations on product family implemented using BDDs, we carry the classification of a document or families of documents using their signatures. Since ML methods are considered to be the de facto standard in document classification and to compare our method performance to their, we implement four ML classification methods: Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (K-NN), and Decision Tree (DT). After that, we merge these modules into one software system called Smart Document Classification System (SDCS). The assessment of our approach to the classification of textual documents shows its f lexibility in indexing and classifying families of textual documents. The classification is deterministic and on a single document (not families of documents), it compares very well with the SVM ML-classifier. Using rules articulated in the language of PFA, It offers a variety of ways for classifying families of documents18 0