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Feature Selection For High Dimensional Data Using Supervised Machine Learning Techniques

Feature Selection For High Dimensional Data Using Supervised...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Lazaros, K. Tasoulis, S. Vrahatis, A. Plagianakos, V. University of Thessaly Dept. of Computer Science and Biomedical Informatics Lamia Greece Ionian University Dept. of Informatics Corfu Greece

ISBN: (纸本)9781665480451

In recent years, feature selection has become an increasingly active field of data science and machine learning research. Most of the datasets that are being used nowadays for various machine learning tasks consist of thousands of features (columns), which make them extremely complex and difficult to work with. In this paper, we propose a feature selection methodological pipeline that can be used to reduce the complexity of high dimensional datasets through the elimination of redundant and/or non-informative features as well as to improve the performance of machine learning models which are trained on high dimensional datasets. The proposed method has been applied to high-dimensional biomedical data and compared against a classic filter-based feature selection algorithm. Specifically, the method was applied to gene expression profiles of a single-cell RNA-seq dataset from healthy and infected by covid-19 human samples. © 2022 IEEE.

关键词： Feature Selection

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Some decidability issues concerning Cn real functions 39

Some decidability issues concerning Cn real functions

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39th Italian Conference on Computational Logic, CILC 2024

作者： Buriola, Gabriele Cantone, Domenico Cincotti, Gianluca Omodeo, Eugenio G. Spartà, Gaetano T. Dept. of Computer Science University of Verona Italy Dept. of Mathematics and Computer Science University of Catania Italy Dept. of Mathematics Informatics and Geosciences University of Trieste Italy Pontifical Gregorian University Rome Italy

This paper adapts preexisting decision algorithms to a family RDF = {RDFa | n ∈ N} of languages regarding one-argument real functions;each RDFn is a quantifier-free theory about the differentiability class Cn, embodying a fragment of Tarskian elementary algebra. The limits of decidability are also highlighted, by pointing out that certain extensions of RDFn are undecidable. The possibility of extending RDFn into a language RDF∞ regarding the class C∞, without disrupting decidability, is briefly discussed. Two sorts of individual variables, namely real variables and function variables, are available in each RDFn. The former are used to construct terms and formulas that involve basic arithmetic operations and comparison relators between real terms, respectively. In contrast, terms designating functions involve function variables, constructs for addition of functions and scalar multiplication, and-outermost-i-th order differentiation Di[ ] with i ≤ n. An array of predicate symbols designate various relationships between functions, as well as function properties, that may hold over intervals of the real line;those are: function comparisons, strict and non-strict monotonicity / convexity / concavity, comparisons between a function (or one of its derivatives) and a real term. The decidability of RDFn relies, on the one hand, on Tarski’s celebrated decision algorithm for the algebra of real numbers, and, on the other hand, on reduction and interpolation techniques. An interpolation method, specifically designed for the case n = 1, has been previously presented;another method, due to Carla Manni, can be used when n = 2. For larger values of n, further research on interpolation is envisaged. © 2024 Copyright for this paper by its authors.

关键词： Computability and decidability

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Challenges of Integrating Blockchain Technology into Educational Institutions

Challenges of Integrating Blockchain Technology into Educati...

引用

2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Ali, Abbas Hussein Yavanoǧlu, Uraz Graduate School of Informatics Gazi University Dept. Of Computer Science Ankara Turkey Gazi University Faculty of Engineering Dept. of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350364910

The latest advancements in blockchain technology have significantly influenced several sectors, such as banking, healthcare, and supply chain networks. Because of its distinct attributes, like decentralization, trustworthiness, and security, this technology has recently been employed in the field of education. While the blockchain is bright, several problems, such as legal, immutability, and scalability questions, continue to hinder its adoption in education. In this paper, we look at educational application system in terms of blockchain-based solutions that have been created for educational purposes, as well as the potential challenges of blockchain technology in the field of education. © 2024 IEEE.

关键词： Blockchain technology educational application

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Design and construction of a medium chamber for a tissue bioreactor system 22

Design and construction of a medium chamber for a tissue bio...

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2024 IEEE 22nd World Symposium on Applied Machine Intelligence and informatics, SAMI 2024

作者： Kolembusova, Petra Ferencik, Norbert Steingartner, William Hudak, Radovan Sedlakova, Veronika Stefanovic, Branko Technical University of Košice Dept. of Biomedical Engineering Košice Slovakia Technical University of Košice Dept. of Computer and Informatics Košice Slovakia

ISBN: (纸本)9798350317206

In the field of tissue engineering and regenerative medicine, understanding the intricate properties and behaviors of cells and tissues within their natural environment is fundamental for translating theory into practical applications. This scientific article introduces the development of a functional medium chamber. The design of this chamber was driven by the need to align it with the volume requirements and specified conditions of bioreactor systems. The objective was to acquire new practical and theoretical insights in the realms of tissue engineering, 3D printing, and bioreactor technology. The emphasis throughout the project was on simplicity in design, with the aim of enabling easy construction and disassembly of the model. This initiative not only involved the creation of a chamber design but also encompassed the 3D printing process and extended functional testing over the long term. The design parameters, including the closing mechanism and chamber shape, were thoughtfully selected to enhance versatility, making the chamber adaptable for various bioreactor systems in the future. Subsequently, the printed chamber underwent rigorous testing using water as the culture medium, laying the groundwork for potential advancements in bioreactor technology and tissue cultivation. © 2024 IEEE.

关键词： Bioreactors

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Co-Operative CNN for Visual Saliency Prediction on WCE Images 48

Co-Operative CNN for Visual Saliency Prediction on WCE Image...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Dimas, George Koulaouzidis, Anastasios Iakovidis, Dimitris K. University of Thessaly Dept. of Computer Science and Biomedical Informatics Greece Odense University Hospital Svendborg Hospital Dept. of Surgery Denmark University of Southern Denmark Dept. of Clinical Research Denmark

ISBN: (纸本)9781728163277

The physician's experience is highly correlated with the content interpretation of medical images. Over time, physicians develop their ability to examine the images, and this is usually reflected on gaze patterns they follow to observe visual cues that lead them to diagnostic decisions. In the context of gaze prediction, graph and machine learning methods have been proposed for the visual saliency estimation on generic images. In this work we preset a novel and robust gaze estimation methodology based on physicians' eye fixations, using convolutional neural networks (CNNs) trained according to a novel co-operative scheme, on medical images acquired during Wireless Capsule Endoscopy (WCE). The proposed training approach considers both the reconstruction accuracy of the estimated saliency maps, and their contribution to the classification process of normal and abnormal findings. The model that was trained with the proposed co-operative procedure was able to achieve an average score of 0.76 Judd's Area Under the receiver operating Characteristic (AUC-J). © 2023 IEEE.

关键词： Co-operative learning Deep Learning Visual Saliency Wireless Capsule Endoscopy

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Using the Tsetlin Machine to Discover Fatality-based Transitions in Political Violence 3

Using the Tsetlin Machine to Discover Fatality-based Transit...

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3rd International Symposium on the Tsetlin Machine, ISTM 2024

作者： Hsu, Hsu-Chiang Bang-Jensen, Bree Colaresi, Michael P. Chrysanthis, Panos K. Zadorozhny, Vladimir I. Dept. of Informatics & Network Systems University of Pittsburgh United States Dept. of Political Science University of Pittsburgh United States Dept. of Computer Science University of Pittsburgh United States

ISBN: (纸本)9798331504984

There is considerable momentum across multiple continents to build and deploy systems that can usefully inform policy-makers and aid agencies about the timing, location, and scale of political violence. In this paper we introduce a framework that utilizes an architecture based on the Tsetlin Machine to detect notable transitions between lower and higher levels of political violence as measured by fatalities. We engineer input features based on the influential framework of horizontal inequalities (HI) and conflict history as they have been theorized to presage violence. However, we encounter the challenge that some classes are more or less distinguishable from others using TM-generated rules. We leverage the analytical structure of TMs and their weights to explain these clusters. To do so, we introduce the concept of a class spectrum and reduce the dimensions of these spectrums across classes and features with principal components analysis (PCA). This pipeline allows us to find transition (change) points between the fatality classes and cluster the classes into efficiently detectable groups. These tools provide both researchers and policy-makers a means of understanding similarities and differences in explanations across different conflict phases. ©2024 IEEE.

关键词： Data warehouses

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ArachNet: Interpretable Sub-Arachnoid Space Segmentation Using an Additive Convolutional Neural Network

ArachNet: Interpretable Sub-Arachnoid Space Segmentation Usi...

引用

IEEE International Symposium on biomedical Imaging

作者： George Dimas Panagiotis G. Kalozoumis Panagiotis Vartholomeos Dimitris K. Iakovidis Dept. of Computer Science and Biomedical Informatics School of Science University of Thessaly

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Considering the growing demand for interpretable deep learning models in medical imaging, this paper introduces ArachNet, a novel Convolutional Neural Network (CNN) architecture tailored for spinal sub-arachnoid space segmentation in MR images. Unlike recent relevant approaches, ArachNet is an inherently interpretable model based on the EPU-CNN interpretable framework. The model necessitates an initial image decomposition into perceptually meaningful components representing structural and anatomical features of the spinal region. Each perceptual component is propagated to a dedicated sub-network that outputs a perceptually meaningful 2D representation, enabling the interpretation of the overall inferred segmentation. The experimental evaluation indicates that ArachNet has great application prospects in the navigation of minimally invasive surgical tools, such as vine robot endoscopes, in the spine.

关键词： Deep learning Image segmentation Minimally invasive surgery Navigation Endoscopes Manuals Image decomposition

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Topic Modeling Using Latent Dirichlet Allocation Method Based On Child Anecdotal Record Data 4

Topic Modeling Using Latent Dirichlet Allocation Method Base...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Fernandis, Rasio Swanjaya, Daniel Ramadhani, Risky Aswi Kasih, Patmi Sahertian, Julian Universitas Nusantara Pgri Kediri Faculty of Engineering and Computer Science Dept. Informatics Engineering Kediri Indonesia

ISBN: (纸本)9798350376111

Kindergarten education has a very important role in shaping the foundation of child development. For parents, see reports on the achievement of children's development at school through the results of each semester's assessment. The process of preparing teacher assessments uses 4 types of assessments, one of which is anecdotal record assessment. However, teachers experience problems in analyzing the topics in children's anecdotal notes so that the results become less clear. In addition, it is still done manually so it takes a long time to analyze. Assessment of children's anecdotal notes is an assessment in the form of a record of events that occur to children while at school. Anecdotal notes are in the form of text data, therefore this research aims to find out the topics in children's anecdotal notes data by utilizing topic modeling using the Latent Dirichlet Allocation (LDA) method. The children's anecdotal notes data is used as one dataset with a total of 276 collected directly from YBPK Sidorejo Kindergarten, after which data preprocessing is carried out in the form of case folding, remove punctuation, tokenizing, stopwords, and stemming. In the LDA topic modeling research using the Gensim library, 5 experiments were carried out by determining the number of different iterations, and displaying a range of 2-10 topics for each experiment. Analysis and evaluation using coherence score to determine the optimal number of topics by looking at the highest coherence score value in the experiment conducted. The analysis results in 3 topics with a coherence score of 0.4704 and 40 iterations or passes. © 2024 IEEE.

关键词： anecdotal record coherence score kindergarten Latent Dirichlet Allocation topic modeling

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Visual Coherence Loss for Coherent and Visually Grounded Story Generation 8

Visual Coherence Loss for Coherent and Visually Grounded Sto...

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8th Workshop on Representation Learning for NLP, RepL4NLP 2023, co-located with ACL 2023

作者： Hong, Xudong Demberg, Vera Sayeed, Asad Zheng, Qiankun Schiele, Bernt Dept. of Computer Vision and Machine Learning MPI Informatics Germany Dept. of Language Science and Technology Dept. of Computer Science Saarland University Germany Dept. of Philosophy Linguistics and Theory of Science University of Gothenburg Sweden Saarland Informatics Campus Saarbrücken Germany

ISBN: (纸本)9781959429777

Local coherence is essential for text generation models. We identify two important aspects of local coherence within the visual storytelling task: (1) the model needs to represent re-occurrences of characters within the image sequence in order to mention them correctly in the story;(2) character representations should enable us to find instances of the same characters and distinguish different characters. In this paper, we propose a loss function inspired by a linguistic theory of coherence for learning image sequence representations. We further propose combining features from an object detector and a face detector to construct stronger character features. To evaluate visual grounding that current reference-based metrics do not measure, we propose a character matching metric to check whether the models generate referring expressions correctly for characters in input image sequences. Experiments on a visual story generation dataset show that our proposed features and loss function are effective for generating more coherent and visually grounded stories. Our code is available at https://***/vwprompt/vcl. © 2023 Association for Computational Linguistics.

关键词： Electric grounding

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Collision-Risk-Aware Ship Routing 24

Collision-Risk-Aware Ship Routing

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32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024

作者： Michalski, Patrik Thomas Preuß, Niko Renz, Matthias Tritsarolis, Andreas Theodoridis, Yannis Pelekis, Nikos Dept. of Computer Science Kiel University Schleswig-Holstein Kiel Germany Dept. of Informatics University of Piraeus Piraeus Greece Dept. of Statistics & Insurance Science University of Piraeus Piraeus Greece

ISBN: (纸本)9798400711077

This paper addresses short-term Collision-Risk-Aware ship route planning while utilizing a deep learning-based Vessel Collision Risk Assessment and Forecasting (VCRA/F) framework to quantify risks. Lacking a clear boundary between risky and viable routes, we propose a Pareto-optimal search for alternative routes, balancing collision risk and voyage time. Our main contribution is a novel framework that integrates VCRA/F for Pareto-optimal route queries in dynamic environments. We model maritime routes using a hexagon-based graph network on the sea. Our experiments on real-world AIS data validate the effectiveness of Skyline-VCRA/F while highlighting areas for further improvement. © 2024 Copyright held by the owner/author(s).

关键词： Ships

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