Design and implementation of multistage tree classifier for Chinese character recognition.

Yeung Lap Kei.Thesis (M.Sc.)--Chinese University of Hong Kong, 1992.Includes bibliographical references (leaves [14-15]).PREFACEABSTRACTCONTENTChapter §1. --- INTRODUCTIONChapter §1.1 --- The Chinese language --- p.1Chapter §1.2 --- Chinese information processing system --- p.2Chapter §1.3 --- Chinese character recognition --- p.4Chapter §1.4 --- Multi-stage tree classifier Vs Single-stage tree classifier in Chinese character recognition --- p.6Chapter §1.5 --- Decision TreeChapter §1.5.1 --- Basic Terminology of a decision tree --- p.7Chapter §1.5.2 --- Structure design of a decision tree --- p.10Chapter §1.6 --- Motivation of the project --- p.12Chapter §1.7 --- Objects of the project --- p.14Chapter §1.8 --- Development environment --- p.14Chapter §2. --- APPROACH 1 - UNSUPERVISED LEARNING --- p.15Chapter §3. --- APPROACH 2 - SUPERVISED LEARNINGChapter §3.1 --- Idea --- p.17Chapter §3.2 --- The 3 Corner Code --- p.20Chapter §3.3 --- Feature Extraction & Selection --- p.22Chapter §3.4 --- Decision at Each NodeChapter §3.4.1 --- Statistical Linear Discriminant Analysis --- p.22Chapter §3.4.2 --- Optimization of the Number of Misclassification --- p.24Chapter §3.5 --- ImplementationChapter §3.5.1 --- Training Data --- p.36Chapter §3.5.2 --- Clustering with the Use of SAS --- p.38Chapter §3.5.3 --- Building the Decision Trees --- p.42Chapter §3.5.4 --- Description of the Classifier --- p.45Chapter §3.6 --- Experiments and Testing ResultChapter §3.6.1 --- Performance Parameters being Measured --- p.47Chapter §3.6.2 --- Testing by Resubstitution Method --- p.50Chapter §3.6.3 --- Noise Model --- p.52Chapter §4. --- POSSIBLE IMPROVEMENT --- p.55Chapter §5. --- EXPERIMENTAL RESULTS & THE IMPROVED MULTISTAGE CLASSIFIERChapter §5.1 --- Experimental Results --- p.59Chapter §5.2 --- Conclusion --- p.70Chapter §6. --- IMPROVED MULTISTAGE TREE CLASSIFIERChapter §6.1 --- The Optimal Multistage Tree Classifier --- p.72Chapter §6.2 --- Performance Analysis --- p.73Chapter §7. --- FURTHER DISCRIMINATION BY CONTEXT CONSIDERATIONChapter §7.1 --- Idea --- p.76Chapter §7.2 --- Description of Algorithm --- p.78Chapter §7.3 --- Performance Analysis --- p.81Chapter §8. --- CONCLUSIONChapter §8.1 --- Advantage of the Classifier --- p.84Chapter §8.2 --- Limitation of the Classifier --- p.85Chapter §9. --- AREA OF FUTURE RESEARCH AND IMPROVEMENTChapter §9.1 --- Detailed Analysis at Each Terminal Node --- p.86Chapter §9.2 --- Improving the Noise Filtering Technique --- p.87Chapter §9.3 --- The Use of 4 Corner Code --- p.88Chapter §9.4 --- Increase in the Dimension of the Feature Space --- p.90Chapter §9.5 --- 1-Tree Protocol with Entropy Reduction --- p.91Chapter §9.6 --- The Use of Human Intelligence --- p.92APPENDICESChapter A.1 --- K-MEANSChapter A.2 --- Unsupervised Learning ApproachChapter A.3 --- Other Algorithms (Maximum Distance & ISODATA)Chapter A.4 --- Possible ImprovementChapter A.5 --- Theories on Statistical Discriminant AnalysisChapter A.6 --- Passage used in Testing the Performance of the Classifier with Context ConsiderationChapter A.7 --- A Partial List of Semantically Related Chinese CharactersChapter A.8 --- An Example of Misclassification TableChapter A.9 --- "Listing of the Program ""CHDIS.C"""REFERENC

Off-line recognition system for printed Chinese characters.

Sin Ka Wai.Thesis (M.Sc.)--Chinese University of Hong Kong, 1992.Includes bibliographical references (leaves [81]-[82]).PREFACEABSTRACTCONTENTChapter §1. --- INTRODUCTIONChapter §1.1 --- The Chinese language --- p.1Chapter §1.2 --- Chinese information processing system --- p.2Chapter §1.3 --- Chinese character recognition --- p.4Chapter §1.4 --- Multi-stage tree classifier Vs Single-stage tree classifier in Chinese character recognition --- p.6Chapter §1.5 --- Decision TreeChapter §1.5.1 --- Basic Terminology of a decision tree --- p.7Chapter §1.5.2 --- Structure design of a decision tree --- p.10Chapter §1.6 --- Motivation of the project --- p.12Chapter §1.7 --- Objects of the project --- p.14Chapter §1.8 --- Development environment --- p.14Chapter §2. --- APPROACH 1 - UNSUPERVISED LEARNINGChapter §2.1 --- Idea --- p.15Chapter §2.2 --- Feature ExtractionChapter §2.2.1 --- Feature selection criteria --- p.15Chapter §2.2.2 --- 4C code --- p.20Chapter §2.2.3 --- Regional code --- p.22Chapter §2.2.4 --- Walsh Transform --- p.24Chapter §2.2.5 --- Black dot density projection profile --- p.26Chapter §2.2.6 --- Corner features --- p.28Chapter §2.3 --- Clustering Method -K-MEANS & Other Algorithms --- p.32Chapter §2.4 --- Pros & Cons --- p.35Chapter §2.5 --- Decision Table --- p.37Chapter §2.6 --- The Optimum Classifier & its Implemen- tation difficulties --- p.39Chapter §3. --- APPROACH 2 - SUPERVISED LEARNING --- p.43Chapter §4. --- POSSIBLE IMPROVEMENTChapter §4.1 --- Training and Test Sample Reduction --- p.46Chapter §4.2 --- Noise Filtering --- p.46Chapter §4.3 --- Decision with Overlapping --- p.52Chapter §4.4 --- Back Tracking for Holes --- p.56Chapter §4.5 --- Fuzzy Decision with Tolerance Limit --- p.59Chapter §4.6 --- Different Tree Architecture --- p.63Chapter §4.7 --- Building Decision Tree by Entropy Reduction Method --- p.65Chapter §5. --- EXPERIMENTAL RESULTS & THE IMPROVED MULTISTAGE CLASSIFIERChapter §5.1 --- Experimental Results --- p.70Chapter §5.2 --- Conclusion --- p.81Chapter §6. --- IMPROVED MULTISTAGE TREE CLASSIFIERChapter §6.1 --- The Optimal Multistage Tree Classifier --- p.83Chapter §6.2 --- Performance Analysis --- p.84Chapter §7. --- FURTHER DISCRIMINATION BY CONTEXT CONSIDERATION --- p.87Chapter §8. --- CONCLUSIONChapter §8.1 --- Advantage of the Classifier --- p.89Chapter §8.2 --- Limitation of the Classifier --- p.90Chapter §9. --- AREA OF FUTURE RESEARCH AND IMPROVEMENTChapter §9.1 --- Detailed Analysis at Each Terminal Node --- p.91Chapter §9.2 --- Improving the Noise Filtering Technique --- p.92Chapter §9.3 --- The Use of 4 Corner Code --- p.93Chapter §9.4 --- Increase in the Dimension of the Feature Space --- p.95Chapter §9.5 --- 1-Tree Protocol with Entropy Reduction --- p.96Chapter §9.6 --- The Use of Human Intelligence --- p.97APPENDICESChapter A.1 --- K-MEANSChapter A.2 --- Maximum Distance Algorithm & ISODATA AlgorithmChapter A.3 --- Approach Two - Supervised LearningChapter A.4 --- Theories on Statistical Discriminant AnalysisChapter A.5 --- An Example of Misclassification TableChapter A.6 --- "Listing of the Program ""CHDIS.C"""Chapter A.7 --- Further Discrimination by Context ConsiderationChapter A.8 --- Passage used in Testing the Performance of the Classifier with Context ConsiderationChapter A.9 --- A Partial List of Semantically Related Chinese CharactersREFERENC

Probabilistic Modeling and Inference for Obfuscated Network Attack Sequences

Prevalent computing devices with networking capabilities have become critical network infrastructure for government, industry, academia and every-day life. As their value rises, the motivation driving network attacks on this infrastructure has shifted from the pursuit of notoriety to the pursuit of profit or political gains, leading to network attack on various scales. Facing diverse network attack strategies and overwhelming alters, much work has been devoted to correlate observed malicious events to pre-defined scenarios, attempting to deduce the attack plans based on expert models of how network attacks may transpire. We started the exploration of characterizing network attacks by investigating how temporal and spatial features of attack sequence can be used to describe different types of attack sources in real data set. Attack sequence models were built from real data set to describe different attack strategies. Based on the probabilistic attack sequence model, attack predictions were made to actively predict next possible actions. Experiments through attack predictions have revealed that sophisticated attackers can employ a number of obfuscation techniques to confuse the alert correlation engine or classifier. Unfortunately, most exiting work treats attack obfuscations by developing ad-hoc fixes to specific obfuscation technique. To this end, we developed an attack modeling framework that enables a systematical analysis of obfuscations. The proposed framework represents network attack strategies as general finite order Markov models and integrates it with different attack obfuscation models to form probabilistic graphical model models. A set of algorithms is developed to inference the network attack strategies given the models and the observed sequences, which are likely to be obfuscated. The algorithms enable an efficient analysis of the impact of different obfuscation techniques and attack strategies, by determining the expected classification accuracy of the obfuscated sequences. The algorithms are developed by integrating the recursion concept in dynamic programming and the Monte-Carlo method. The primary contributions of this work include the development of the formal framework and the algorithms to evaluate the impact of attack obfuscations. Several knowledge-driven attack obfuscation models are developed and analyzed to demonstrate the impact of different types of commonly used obfuscation techniques. The framework and algorithms developed in this work can also be applied to other contexts beyond network security. Any behavior sequences that might suffer from noise and require matching to pre-defined models can use this work to recover the most likely original sequence or evaluate quantitatively the expected classification accuracy one can achieve to separate the sequences

A Study on Comparison of Classification Algorithms for Pump Failure Prediction

The reliability of pumps can be compromised by faults, impacting their functionality. Detecting these faults is crucial, and many studies have utilized motor current signals for this purpose. However, as pumps are rotational equipped, vibrations also play a vital role in fault identification. Rising pump failures have led to increased maintenance costs and unavailability, emphasizing the need for cost-effective and dependable machinery operation. This study addresses the imperative challenge of defect classification through the lens of predictive modeling. With a problem statement centered on achieving accurate and efficient identification of defects, this study’s objective is to evaluate the performance of five distinct algorithms: Fine Decision Tree, Medium Decision Tree, Bagged Trees (Ensemble), RUS-Boosted Trees, and Boosted Trees. Leveraging a comprehensive dataset, the study meticulously trained and tested each model, analyzing training accuracy, test accuracy, and Area Under the Curve (AUC) metrics. The results showcase the supremacy of the Fine Decision Tree (91.2% training accuracy, 74% test accuracy, AUC 0.80), the robustness of the Ensemble approach (Bagged Trees with 94.9% training accuracy, 99.9% test accuracy, and AUC 1.00), and the competitiveness of Boosted Trees (89.4% training accuracy, 72.2% test accuracy, AUC 0.79) in defect classification. Notably, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and k-Nearest Neighbors (KNN) exhibited comparatively lower performance. Our study contributes valuable insights into the efficacy of these algorithms, guiding practitioners toward optimal model selection for defect classification scenarios. This research lays a foundation for enhanced decision-making in quality control and predictive maintenance, fostering advancements in the realm of defect prediction and classification

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition