424 research outputs found
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
Advanced analytical methods for fraud detection: a systematic literature review
The developments of the digital era demand new ways of producing goods and rendering
services. This fast-paced evolution in the companies implies a new approach from the
auditors, who must keep up with the constant transformation. With the dynamic
dimensions of data, it is important to seize the opportunity to add value to the companies.
The need to apply more robust methods to detect fraud is evident.
In this thesis the use of advanced analytical methods for fraud detection will be
investigated, through the analysis of the existent literature on this topic.
Both a systematic review of the literature and a bibliometric approach will be applied to
the most appropriate database to measure the scientific production and current trends.
This study intends to contribute to the academic research that have been conducted, in
order to centralize the existing information on this topic
Northeastern Illinois University, Academic Catalog 2023-2024
https://neiudc.neiu.edu/catalogs/1064/thumbnail.jp
Deep Neural Networks and Tabular Data: Inference, Generation, and Explainability
Over the last decade, deep neural networks have enabled remarkable technological advancements, potentially transforming a wide range of aspects of our lives in the future. It is becoming increasingly common for deep-learning models to be used in a variety of situations in the modern life, ranging from search and recommendations to financial and healthcare solutions, and the number of applications utilizing deep neural networks is still on the rise.
However, a lot of recent research efforts in deep learning have focused primarily on neural networks and domains in which they excel. This includes computer vision, audio processing, and natural language processing. It is a general tendency for data in these areas to be homogeneous, whereas heterogeneous tabular datasets have received relatively scant attention despite the fact that they are extremely prevalent. In fact, more than half of the datasets on the Google dataset platform are structured and can be represented in a tabular form.
The first aim of this study is to provide a thoughtful and comprehensive analysis of deep neural networks' application to modeling and generating tabular data. Apart from that, an open-source performance benchmark on tabular data is presented, where we thoroughly compare over twenty machine and deep learning models on heterogeneous tabular datasets.
The second contribution relates to synthetic tabular data generation. Inspired by their success in other homogeneous data modalities, deep generative models such as variational autoencoders and generative adversarial networks are also commonly applied for tabular data generation. However, the use of Transformer-based large language models (which are also generative) for tabular data generation have been received scant research attention. Our contribution to this literature consists of the development of a novel method for generating tabular data based on this family of autoregressive generative models that, on multiple challenging benchmarks, outperformed the current state-of-the-art methods for tabular data generation.
Another crucial aspect for a deep-learning data system is that it needs to be reliable and trustworthy to gain broader acceptance in practice, especially in life-critical fields. One of the possible ways to bring trust into a data-driven system is to use explainable machine-learning methods.
In spite of this, the current explanation methods often fail to provide robust explanations due to their high sensitivity to the hyperparameter selection or even changes of the random seed. Furthermore, most of these methods are based on feature-wise importance, ignoring the crucial relationship between variables in a sample. The third aim of this work is to address both of these issues by offering more robust and stable explanations, as well as taking into account the relationships between variables using a graph structure.
In summary, this thesis made a significant contribution that touched many areas related to deep neural networks and heterogeneous tabular data as well as the usage of explainable machine learning methods
Risk assessment for progression of Diabetic Nephropathy based on patient history analysis
A nefropatia diabética (ND) é uma das complicações mais comuns em doentes com
diabetes. Trata-se de uma doença crónica que afeta progressivamente os rins,
podendo resultar numa insuficiência renal. A digitalização permitiu aos hospitais
armazenar as informações dos doentes em registos de saúde eletrónicos (RSE). A
aplicação de algoritmos de Machine Learning (ML) a estes dados pode permitir a
previsão do risco na evolução destes doentes, conduzindo a uma melhor gestão da
doença. O principal objetivo deste trabalho é criar um modelo preditivo que tire
partido do historial do doente presente nos RSE. Foi aplicado neste trabalho o maior
conjunto de dados de doentes portugueses com DN, seguidos durante 22 anos pela
Associação Protetora dos Diabéticos de Portugal (APDP). Foi desenvolvida uma
abordagem longitudinal na fase de pré-processamento de dados, permitindo que
estes fossem servidos como entrada para dezasseis algoritmos de ML distintos. Após
a avaliação e análise dos respetivos resultados, o Light Gradient Boosting Machine
foi identificado como o melhor modelo, apresentando boas capacidades de previsão.
Esta conclusão foi apoiada não só pela avaliação de várias métricas de classificação
em dados de treino, teste e validação, mas também pela avaliação do seu
desempenho por cada estádio da doença. Para além disso, os modelos foram
analisados utilizando gráficos de feature ranking e através de análise estatística.
Como complemento, são ainda apresentados a interpretabilidade dos resultados
através do método SHAP, assim como a distribuição do modelo utilizando o Gradio
e os servidores da Hugging Face. Através da integração de técnicas ML, de um
método de interpretação e de uma aplicação Web que fornece acesso ao modelo,
este estudo oferece uma abordagem potencialmente eficaz para antecipar a evolução
da ND, permitindo que os profissionais de saúde tomem decisões informadas para
a prestação de cuidados personalizados e gestão da doença
Geographic information extraction from texts
A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction
Graduate Catalog, 2023-2024
https://scholar.valpo.edu/gradcatalogs/1060/thumbnail.jp
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
주요 우울 장애의 음성 기반 분석: 연속적인 발화의 음향적 변화를 중심으로
학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 융합과학부(디지털정보융합전공), 2023. 2. 이교구.Major depressive disorder (commonly referred to as depression) is a common disorder that affects 3.8% of the world's population. Depression stems from various causes, such as genetics, aging, social factors, and abnormalities in the neurotransmitter system; thus, early detection and monitoring are essential. The human voice is considered a representative biomarker for observing depression; accordingly, several studies have developed an automatic depression diagnosis system based on speech.
However, constructing a speech corpus is a challenge, studies focus on adults under 60 years of age, and there are insufficient medical hypotheses based on the clinical findings of psychiatrists, limiting the evolution of the medical diagnostic tool. Moreover, the effect of taking antipsychotic drugs on speech characteristics during the treatment phase is overlooked.
Thus, this thesis studies a speech-based automatic depression diagnosis system at the semantic level (sentence). First, to analyze depression among the elderly whose emotional changes do not adequately reflect speech characteristics, it developed the mood-induced sentence to build the elderly depression speech corpus and designed an automatic depression diagnosis system for the elderly.
Second, it constructed an extrapyramidal symptom speech corpus to investigate the extrapyramidal symptoms, a typical side effect that can appear from an antipsychotic drug overdose. Accordingly, there is a strong correlation between the antipsychotic dose and speech characteristics. The study paved the way for a comprehensive examination of the automatic diagnosis system for depression.주요 우울 장애 즉 흔히 우울증이라고 일컬어지는 기분 장애는 전 세계인 중 3.8%에 달하는 사람들이 겪은바 있는 매우 흔한 질병이다. 유전, 노화, 사회적 요인, 신경전달물질 체계의 이상등 다양한 원인으로 발생하는 우울증은 조기 발견 및 일상 생활에서의 관리가 매우 중요하다고 할 수 있다. 인간의 음성은 우울증을 관찰하기에 대표적인 바이오마커로 여겨져 왔으며, 음성 데이터를 기반으로한 자동 우울증 진단 시스템 개발을 위한 여러 연구들이 진행되어 왔다. 그러나 음성 말뭉치 구축의 어려움과 60세 이하의 성인들에게 초점이 맞추어진 연구, 정신과 의사들의 임상 소견을 바탕으로한 의학적 가설 설정의 미흡등의 한계점을 가지고 있으며, 이는 의료 진단 기구로 발전하는데 한계점이라고 할 수 있다. 또한, 항정신성 약물의 복용이 음성 특징에 미칠 수 있는 영향 또한 간과되고 있다.
본 논문에서는 위의 한계점들을 보완하기 위한 의미론적 수준 (문장 단위)에서의 음성 기반 자동 우울증 진단에 대한 연구를 시행하고자 한다. 우선적으로 감정의 변화가 음성 특징을 잘 반영되지 않는 노인층의 우울증 분석을 위해 감정 발화 문장을 개발하여 노인 우울증 음성 말뭉치를 구축하고, 문장 단위에서의 관찰을 통해 노인 우울증 군에서 감정 문장 발화가 미치는 영향과 감정 전이를 확인할 수 있었으며, 노인층의 자동 우울증 진단 시스템을 설계하였다. 최종적으로 항정신병 약물의 과복용으로 나타날 수 있는 대표적인 부작용인 추체외로 증상을 조사하기 위해 추체외로 증상 음성 말뭉치를 구축하였고, 항정신병 약물의 복용량과 음성 특징간의 상관관계를 분석하여 우울증의 치료 과정에서 항정신병 약물이 음성에 미칠 수 있는 영향에 대해서 조사하였다. 이를 통해 주요 우울 장애의 영역에 대한 포괄적인 연구를 진행하였다.Chapter 1 Introduction 1
1.1 Research Motivations 3
1.1.1 Bridging the Gap Between Clinical View and Engineering 3
1.1.2 Limitations of Conventional Depressed Speech Corpora 4
1.1.3 Lack of Studies on Depression Among the Elderly 4
1.1.4 Depression Analysis on Semantic Level 6
1.1.5 How Antipsychotic Drug Affects the Human Voice? 7
1.2 Thesis objectives 9
1.3 Outline of the thesis 10
Chapter 2 Theoretical Background 13
2.1 Clinical View of Major Depressive Disorder 13
2.1.1 Types of Depression 14
2.1.2 Major Causes of Depression 15
2.1.3 Symptoms of Depression 17
2.1.4 Diagnosis of Depression 17
2.2 Objective Diagnostic Markers of Depression 19
2.3 Speech in Mental Disorder 19
2.4 Speech Production and Depression 21
2.5 Automatic Depression Diagnostic System 23
2.5.1 Acoustic Feature Representation 24
2.5.2 Classification / Prediction 27
Chapter 3 Developing Sentences for New Depressed Speech Corpus 31
3.1 Introduction 31
3.2 Building Depressed Speech Corpus 32
3.2.1 Elements of Speech Corpus Production 32
3.2.2 Conventional Depressed Speech Corpora 35
3.2.3 Factors Affecting Depressed Speech Characteristics 39
3.3 Motivations 40
3.3.1 Limitations of Conventional Depressed Speech Corpora 40
3.3.2 Attitude of Subjects to Depression: Masked Depression 43
3.3.3 Emotions in Reading 45
3.3.4 Objectives of this Chapter 45
3.4 Proposed Methods 46
3.4.1 Selection of Words 46
3.4.2 Structure of Sentence 47
3.5 Results 49
3.5.1 Mood-Inducing Sentences (MIS) 49
3.5.2 Neutral Sentences for Extrapyramidal Symptom Analysis 49
3.6 Summary 51
Chapter 4 Screening Depression in The Elderly 52
4.1 Introduction 52
4.2 Korean Elderly Depressive Speech Corpus 55
4.2.1 Participants 55
4.2.2 Recording Procedure 57
4.2.3 Recording Specification 58
4.3 Proposed Methods 59
4.3.1 Voice-based Screening Algorithm for Depression 59
4.3.2 Extraction of Acoustic Features 59
4.3.3 Feature Selection System and Distance Computation 62
4.3.4 Classification and Statistical Analyses 63
4.4 Results 65
4.5 Discussion 69
4.6 Summary 74
Chapter 5 Correlation Analysis of Antipsychotic Dose and Speech Characteristics 75
5.1 Introduction 75
5.2 Korean Extrapyramidal Symptoms Speech Corpus 78
5.2.1 Participants 78
5.2.2 Recording Process 79
5.2.3 Extrapyramidal Symptoms Annotation and Equivalent Dose Calculations 80
5.3 Proposed Methods 81
5.3.1 Acoustic Feature Extraction 81
5.3.2 Speech Characteristics Analysis recording to Eq.dose 83
5.4 Results 83
5.5 Discussion 87
5.6 Summary 90
Chapter 6 Conclusions and Future Work 91
6.1 Conclusions 91
6.2 Future work 95
Bibliography 97
초 록 121박
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