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Artificial Intelligence and Data Engineering (AIDE), international conference on

作者： Alapati Naga Sree Vaishnavi Neha Lalitha Somesetty Challa Sai Muni Ganesh Reddy Danda Vamsi Chakradhar Vennampalli Venkateswarluu M. Srinivas Department of Computer Science Amrita Vishwa Vidyapeetham Amaravati India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

The COVID-19 pandemic emphasizes the critical need for rapid and reliable diagnostic procedures to improve infection detection and prevention. This study compares Machine Learning (ML), Deep Learning(DL) and probabilistic graphical models (PGMs) for predicting COVID-19 infection using a publicly available dataset. ML models such as Support Vector Machine(SVM), Random Forest(RF), Decision Tree(DT), K-Nearest Neighbour(KNN), Multi Layer Perceptron(MLP) and Voting Classifier(VC) with Synthetic Minority Over sampling Technique (SMOTE) attained an accuracy of 98.9%. The Deep Neural Network (DNN) attained 97.88% accuracy and Bayesian Network(BN)implemented using probabilistic graphical models in Python(PGMPY) achieved 96 % accuracy. Despite lower accuracy, BN helps in complex diagnostic tasks. It also demonstrates the power of ensemble ML and DL models. Therefore these results reflect the advantages attained from ensemble ML,DL and BN models in terms of flexibility and the ability of each approach to solve complex diagnostic problems and tasks, as represented by the predictive modeling of COVID-19 cases.

关键词： COVID-19 Accuracy graphical models Computational modeling Medical services Predictive models probabilistic logic Data models Real-time systems Bayes methods

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20th international conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2024

20th International Conference on Information Processing and ...

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20th international conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2024

ISBN: (纸本)9783031739965

The proceedings contain 33 papers. The special focus in this conference is on Information Processing and Management of Uncertainty in Knowledge-Based Systems. The topics include: graphical Causal models with Discretized Data and Background Information;enhancing the Intelligibility of Boolean Decision Trees with Concise and Reliable probabilistic Explanations;towards an Interpretable Fuzzy Approach to Experimental Design;from Default to Analogical and Paralogical Reasoning. Logics of Pairs and Their Multiple-Valued Extensions;lot Sizing Problem Under Lead-Time Uncertainty;forced Periodic Optimal Scheduling Policy for Graph Reinforcement Learning;similarity Relations Based Numerical Algorithm for Solving Maximin Problems;possibilistic Approach for Meta-analysis;Dual Resource Flexible Job Shop Scheduling Problems: The xBTF Algorithm;information Retrieval Using Fuzzy Fingerprints;Handling Veracity of SVM Predictions;interval Criterion-Based Evidential Set-Valued Classification;analysis of the φ-Index of Inclusion Restricted to a Set of Indexes;enhancing Bayesian Network Structural Learning with Monte Carlo Tree Search;from Quantifying and Propagating Uncertainty to Quantifying and Propagating Both Uncertainty and Reliability: Practice-Motivated Approach to Measurement Planning and Data Processing;extending Idioms for Bayesian Network Construction with Qualitative Constraints;optimizing Causal Interventions in Hybrid Bayesian Networks: A Discretization, Knowledge Compilation, and Heuristic Optimization Approach;visualization of Preference Matrices for Labeled Objects;assessing Causal Graph Uncertainty and Optimized Multivariate Discretization Strategy;exploring Word Embedding in Modeling Risk Perception;similarity of Concepts in Weighted Knowledge Graphs;evidential Linear Regression for Soft Detrended Fluctuation Analysis;COPILS: COmParIson of Linguistic Summaries;groupoids in Categories of Fuzzy Topological Spaces with Continuous Fuzzy Relations;preface;On the Use of Mo

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20th international conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2024

20th International Conference on Information Processing and ...

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20th international conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2024

ISBN: (纸本)9783031739996

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Personalized Healthcare: Utilizing probabilistic models for Disease-Drug Association and Treatment Prediction

Personalized Healthcare: Utilizing Probabilistic Models for ...

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conference on Data Science and Machine Learning Applications (CDMA)

作者： Sarah Abed Alsubhi Mashael Al-Luhaybi Rawabi Abod ALlihyani Raghad Abdulaziz Alharbi Futun Salem Alharbi Batool Mohammed Alharbi Computer Science Department JUC Umm Al-Qura University Makkah Saudi Arabia

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

The current healthcare system faces challenges in delivering treatment recommendations personalized to individual patient needs, leading to issues such as misdiagnosis, delayed treatment plans, and harmful drug interactions. In response to these challenges, personalized medicine has emerged as a critical component in healthcare, aiming to provide patient-specific clinical treatment recommendations based on individual conditions, thereby enabling more accurate diagnoses and treatments. Machine Learning (ML) has proven to be a crucial approach in advancing personalized medicine and healthcare. Numerous ML algorithms have been implemented to generate suitable recommendations tailored to individual patient conditions. However, most of these algorithms lack explain ability and reasoning behind their decisions, often relying on advanced black-box models. In this paper, we implement the Bayesian networks algorithm, which utilizes a probabilistic learning approach that is both explainable and effective due to its ability to learn, represent relationships, and exploit correlations between variables, thereby enabling ethically informed predictions of risks and side effects. Using graphical models, healthcare providers can deliver individualized care by proposing methods and treatment plans adapted to each patient's specific needs and conditions. This approach aims to minimize side effects and provide precise treatment recommendations, thereby enhancing overall patient care. We conducted three experiments to develop explainable predictive models, exploring three predictive classes: drug class, drug activity, and the side effects of drugs associated with diseases such as Allergies, Alzheimer's, Cancer, and Stroke, etc. The predictive models achieved high accuracy rates, ranging from 82% to 99%, and obtained very reasonable validation accuracies.

关键词： Drugs Adaptation models Accuracy Machine learning algorithms Precision medicine Predictive models Prediction algorithms probabilistic logic Data models Bayes methods

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Special Issue on the Seventh probabilistic graphical models conference (PGM 2014)

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international JOURNAL OF APPROXIMATE REASONING 2016年第Jan.期68卷 88-90页

作者： Renooij, Silja Univ Utrecht Dept Informat & Comp Sci Utrecht Netherlands

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international Workshop on Medical Computer Vision, MCV 2016, and of the international Workshop on Bayesian and graphical models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016

International Workshop on Medical Computer Vision, MCV 2016,...

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international Workshop on Medical Computer Vision, MCV 2016, and of the international Workshop on Bayesian and graphical models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016

ISBN: (纸本)9783319611877

The proceedings contain 19 papers. The special focus in this conference is on Medical Computer Vision. The topics include: Automated cortical parcellation and comparison with existing brain atlases;inferring disease status by non-parametric probabilistic embedding;a lung graph-model for pulmonary hypertension and pulmonary embolism detection on DECT images;explaining radiological emphysema subtypes with unsupervised texture prototypes;automatic segmentation of abdominal MRI using selective sampling and random walker;a pilot study for integrating eye-tracking technology into medical image segmentation;automatic detection of histological artifacts in mouse brain slice images;lung nodule classification by jointly using visual descriptors and deep features;representation learning for cross-modality classification;guideline based machine learning for standard plane extraction in 3D cardiac ultrasound;a statistical model for simultaneous template estimation, bias correction, and registration of 3D brain images;Bayesian multiview manifold learning applied to hippocampus shape and clinical score data;rigid slice-to-volume medical image registration through Markov random fields;non-local graph-based regularization for deformable image registration and unsupervised framework for consistent longitudinal MS lesion segmentation.

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aGrUM/pyAgrum : a Toolbox to Build models and Algorithms for probabilistic graphical models in Python 10

aGrUM/pyAgrum : a Toolbox to Build Models and Algorithms for...

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10th international conference on probabilistic graphical models (PGM)

作者： Ducamp, Gaspard Gonzales, Christophe Wuillemin, Pierre-Henri Sorbonne Univ LIP6 4 Pl Jussieu F-75005 Paris France Univ Toulon & Var Aix Marseille Univ CNRS LIS Marseille France

This paper presents the aGrUM framework, a LGPL C++ library providing state-of-the-art implementations of graphical models for decision making, including Bayesian Networks, Markov Networks (Markov random fields), Influence Diagrams, Credal Networks, probabilistic Relational models. The framework also contains a wrapper, pyAgrum for exploiting aGrUM in Python. This framework is the result of an ongoing effort to build an efficient and well maintained open source cross-platform software, running on Linux, MacOS X and Windows, for dealing with graphical models and for providing essential components to build new algorithms for graphical models.

关键词： Bayesian Networks probabilistic graphical models c plus python

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A Systematic Review on Hidden Markov models for Sentiment Analysis 15

A Systematic Review on Hidden Markov Models for Sentiment An...

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15th international conference on Electronics, Computer and Computation (ICECCO)

作者： Odumuyiwa, Victor Osisiogu, Ukachi Univ Lagos Dept Comp Sci Lagos Nigeria African Univ Sci & Technol Dept Comp Sci Abuja Nigeria

ISBN: (纸本)9781728151601

This paper gives a review of the literature on the application of Hidden Markov models in the field of sentiment analysis. This is done in relation to a research project on semantic representation and the use of probabilistic graphical models for the determination of sentiment in textual data. Relevant articles have been analyzed that correspond mainly to the certain variations of the implementation of HMM and a variety of use cases for the purpose of sentiment classification. Finally, this review presents the grounds for future works that seek to develop techniques for semantic text representations implemented with probabilistic graphical models (Hidden Markov models) or that through a combination scheme allow for superior classification performance.

关键词： Hidden Markov models Markov Chain Sentiment Analysis probabilistic graphical models Review

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Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena

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JOURNAL OF COMPUTATIONAL SCIENCE 2019年 30卷 1-10页

作者： Ramazzotti, Daniele Nobile, Marco S. Antoniotti, Marco Graudenzi, Alex Stanford Univ Dept Pathol Stanford CA 94305 USA Univ Milano Bicocca Dept Informat Syst & Commun Milan Italy SYSBIO Ctr Syst Biol Milan Italy Univ Milano Bicocca Milan Ctr Neurosci Monza Italy

Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is further complicated by many theoretical issues, such as the I-equivalence among different structures. In this work, we focus on a specific subclass of BNs, named Suppes-Bayes Causal Networks (SBCNs), which include specific structural constraints based on Suppes' probabilistic causation to efficiently model cumulative phenomena. Here we compare the performance, via extensive simulations, of various state-of-the-art search strategies, such as local search techniques and Genetic Algorithms, as well as of distinct regularization methods. The assessment is performed on a large number of simulated datasets from topologies with distinct levels of complexity, various sample size and different rates of errors in the data. Among the main results, we show that the introduction of Suppes' constraints dramatically improve the inference accuracy, by reducing the solution space and providing a temporal ordering on the variables. We also report on trade-offs among different search techniques that can be efficiently employed in distinct experimental settings. This manuscript is an extended version of the paper "Structural Learning of probabilistic graphical models of Cumulative Phenomena" presented at the 2018 international conference on Computational Science [1]. (C) 2018 Elsevier B.V. All rights reserved.

关键词： graphical models Structural learning Causality Suppes-Bayes Causal Networks Cumulative phenomena

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Constructing probabilistic models

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international JOURNAL OF MEDICAL INFORMATICS 1997年第1-2期45卷 9-18页

作者： Jirousek, R Kushmerick, N AV CR INST INFORMAT & AUTOMAT PRAGUE CZECH REPUBLIC UNIV WASHINGTON DEPT COMP SCI SEATTLE WA 98195 USA

Bayesian networks have become one of the most popular probabilistic techniques in AI, largely due to the development of several efficient inference algorithms. In this paper we describe a heuristic method for constructing Bayesian networks. Our construction method relies on the relationship between Bayesian networks and decomposable models, a special kind of graphical model. We explain this relationship and then show how it can be used to facilitate model construction. Finally, we describe an implemented computer program that illustrates these ideas. (C) 1997 Elsevier Science Ireland Ltd.

关键词： uncertainty probability theory graph models Bayesian network learning

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