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arXiv 2024年

作者： Michelucci, Umberto Venturini, Francesca Artificial Intelligence Research and Development TOELT LLC Duebendorf Switzerland Computer Science Department Lucerne University of Applied Sciences and Arts Rotkreuz Switzerland Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Winterthur Switzerland

Fluorescence spectroscopy is a fundamental tool in life sciences and chemistry, widely used for applications such as environmental monitoring, food quality control, and biomedical diagnostics. However, analysis of spectroscopic data with deep learning, in particular of fluorescence excitation-emission matrices (EEMs), presents significant challenges due mainly to the typically small and sparse datasets available. Furthermore, the analysis of EEMs is difficult due to their high dimensionality and overlapping spectral features. This study proposes a new approach that exploits domain adaptation with pretrained vision models, alongside a novel interpretability algorithm to address these challenges. Thanks to specialised feature engineering of the neural networks described in this work, we are now able to provide deeper and meaningful insights into the physico-chemical processes underlying the data. The proposed approach is demonstrated through the analysis of the oxidation process in extra virgin olive oil (EVOO), showing its effectiveness in predicting quality indicators and identifying relevant spectral bands. This work describes significantly innovative results in the use of deep learning for spectroscopy, transforming it from a black box into a tool for understanding complex biological and chemical processes. © 2024, CC BY-NC-ND.

关键词： Fluorescence spectroscopy

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4D reconstruction of JET DTE2 fast-ion distribution function based on synthetic data 50

4D reconstruction of JET DTE2 fast-ion distribution function...

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50th EPS Conference on Plasma Physics, EPS 2024

作者： Rud, M. Hyvärinen, O. Järleblad, H. Snicker, A. Eriksson, J. Dong, Y. Hansen, P.C. Nocente, M. Reman, B. Schmidt, B.S. Valentini, A. Moseev, D. Salewski, M. Department of Physics Technical University of Denmark Kgs Lyngby2800 Denmark Department of Mathematics and Statistics University of Helsinki FI-00014 Finland Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby2800 Denmark VTT Technical Research Center of Finland Finland Uppsala University Uppsala751 20 Sweden Department of Physics University of Milano-Bicocca Milano20126 Italy Max-Planck-Institut für Plasmaphysik Wendelsteinstr. 1 Greifswald17491 Germany

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Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

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computers, Materials & Continua 2022年第11期73卷 2423-2439页

作者： Sara A.Althubiti JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Romany F.Mansour Fayadh Alenezi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Research Center-CIENS Escuela Naval de Suboficiales A.R.C.“Barranquilla”BarranquillaColombia Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga 72511Egypt Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several *** Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality *** OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate *** this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart *** primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and ***,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the *** addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature ***,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification *** design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s *** experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

关键词： Ovarian cancer decision support system smart healthcare IoMT deep learning feature selection

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Challenges in explaining deep learning models for data with biological variation

arXiv

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arXiv 2024年

作者： Tětková, Lenka Dreier, Erik Schou Malm, Robin Hansen, Lars Kai Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby2800 Denmark FOSS Analytical A/S Hillerød3400 Denmark

Much machine learning research progress is based on developing models and evaluating them on a benchmark dataset (e.g., ImageNet for images). However, applying such benchmark-successful methods to real-world data often does not work as expected. This is particularly the case for biological data where we expect variability at multiple time and spatial scales. Typical benchmark data has simple, dominant semantics, such as a number, an object type, or a word. In contrast, biological samples often have multiple semantic components leading to complex and entangled signals. Complexity is added if the signal of interest is related to atypical states, e.g., disease, and if there is limited data available for learning. In this work, we focus on image classification of real-world biological data that are, indeed, different from standard images. We are using grain data and the goal is to detect diseases and damages, for example, "pink fusarium" and "skinned". Pink fusarium, skinned grains, and other diseases and damages are key factors in setting the price of grains or excluding dangerous grains from food production. Apart from challenges stemming from differences of the data from the standard toy datasets, we also present challenges that need to be overcome when explaining deep learning models. For example, explainability methods have many hyperparameters that can give different results, and the ones published in the papers do not work on dissimilar images. Other challenges are more general: problems with visualization of the explanations and their comparison since the magnitudes of their values differ from method to method. An open fundamental question also is: How to evaluate explanations? It is a non-trivial task because the "ground truth" is usually missing or ill-defined. Also, human annotators may create what they think is an explanation of the task at hand, yet the machine learning model might solve it in a different and perhaps counter-intuitive way. We discuss severa

关键词： Deep learning

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Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance

arXiv

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arXiv 2024年

作者： Oikonomou, Dimitris Loizou, Nicolas Johns Hopkins University United States Department of Computer Science Johns Hopkins University United States Department of Applied Mathematics and Statistics Johns Hopkins University United States

Stochastic gradient descent with momentum, also known as Stochastic Heavy Ball method (SHB), is one of the most popular algorithms for solving large-scale stochastic optimization problems in various machine learning tasks. In practical scenarios, tuning the step-size and momentum parameters of the method is a prohibitively expensive and time-consuming process. In this work, inspired by the recent advantages of stochastic Polyak step-size in the performance of stochastic gradient descent (SGD), we propose and explore new Polyak-type variants suitable for the update rule of the SHB method. In particular, using the Iterate Moving Average (IMA) viewpoint of SHB, we propose and analyze three novel step-size selections: MomSPSmax, MomDecSPS, and MomAdaSPS. For MomSPSmax, we provide convergence guarantees for SHB to a neighborhood of the solution for convex and smooth problems (without assuming interpolation). If interpolation is also satisfied, then using MomSPSmax, SHB converges to the true solution at a fast rate matching the deterministic HB. The other two variants, MomDecSPS and MomAdaSPS, are the first adaptive step-sizes for SHB that guarantee convergence to the exact minimizer without prior knowledge of the problem parameters and without assuming interpolation. The convergence analysis of SHB is tight and obtains the convergence guarantees of SGD with stochastic Polyak step-sizes as a special case. We supplement our analysis with experiments that validate the theory and demonstrate the effectiveness and robustness of the new algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Interpolation

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Study and prediction of prostate cancer using fuzzy inference system

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Materials Today: Proceedings 2022年 56卷 157-164页

作者： Boadh, Rahul Aarya, Deo Datta Dahiya, Mamta Rathee, Rakesh Rathee, Sarita Kumar, Ajay Jain, Sarika Rajoria, Yogendra Kumar Department of the Mathematics School of Basic and Applied Science K. R. Mangalam University Sohna Road Haryana Gurugram122103 India Department of the Mathematics Acharya Narendra Dev College University of Delhi India Department of Computer Science & Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122103 India University Institute of Engineering & Technology Maharshi Dayanand University Haryana 124001 Rohtak India Department of Electrical Engineering DPG Institute of Technology and Management Haryana Gurgaon122001 India Department of Mechanical Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122103 India Department of Mathematics Amity School of Applied Sciences Amity University Haryana Gurugram122413 India

The essential driver of passing among men on the planet is prostate disease. In the chief stage, for early identification of the prostate disease by applying Fuzzy Inference System (FIS) has been depicted in this study. The age of the patient, Prostate Volume (PV), % Free PSA (FPSA) and Prostate Specific Antigen (PSA) are utilized as input parameters in FIS for computing the risk factor of the prostate cancer. In this study Mamdani method is used for evaluation and analysis of the Prostate Cancer Risk (PCR). The present FIS additionally required the base estimation decreases horribleness lessens dismalness, mortality and more dependable from other system. It can likewise be considered in medicine to decrease manual assignments and human mistake. This review advocate that, this FIS might consequence on work on the productivity to recognized the distinctive kind of the malignant growths just as lessening the expense of care of the patients. © 2022

关键词： Risk assessment

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EyeLoveGAN: Exploiting domain-shifts to boost network learning with cycleGANs

arXiv

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arXiv 2022年

作者： Sundgaard, Josefine Vilsbøll Juhl, Kristine Aavild Slipsager, Jakob Mølkjær Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark

This paper presents our contribution to the REFUGE challenge 2020. The challenge consisted of three tasks based on a dataset of retinal images: Segmentation of optic disc and cup, classification of glaucoma, and localization of fovea. We propose employing convolutional neural networks for all three tasks. Segmentation is performed using a U-Net, classification is performed by a pre-trained InceptionV3 network, and fovea detection is performed by employing stacked hour-glass for heatmap prediction. The challenge dataset contains images from three different data sources. To enhance performance, cycleGANs were utilized to create a domain-shift between the data sources. These cycleGANs move images across domains, thus creating artificial images which can be used for training. © 2022, CC BY.

关键词： Ophthalmology

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Critical nonlinear aspects of hopping transport for reconfigurable logic in disordered dopant networks

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Physical Review applied 2024年第2期22卷 024063页

作者： Henri Tertilt Jonas Mensing Marlon Becker Wilfred G. van der Wiel Peter A. Bobbert Andreas Heuer Institute for Physical Chemistry Department of Physics NanoElectronics Group Faculty of Electrical Engineering Mathematics and Computer Science MESA+ Institute for Nanotechnology and Center for Brain-Inspired Nano Systems (BRAINS) Department of Applied Physics Center for Computational Energy Research (CCER)

Nonlinear behavior in the hopping transport of interacting charges enables reconfigurable logic in disordered dopant network devices, where voltages applied at control electrodes tune the relation between voltages applied at input electrodes and the current measured at an output electrode. From kinetic Monte Carlo simulations we analyze the critical nonlinear aspects of variable-range hopping transport for realizing Boolean logic gates in these devices on three levels. First, we quantify the occurrence of individual gates for random choices of control voltages. We find that linearly inseparable gates such as the xor gate are less likely to occur than linearly separable gates such as the and gate, despite the fact that the number of different regions in the multidimensional control voltage space for which and or xor gates occur is comparable. Second, we use principal-component analysis to characterize the distribution of the output current vectors for the (00,10,01,11) logic input combinations in terms of eigenvectors and eigenvalues of the output covariance matrix. This allows a simple and direct comparison of the behavior of different simulated devices and a comparison to experimental devices. Third, we quantify the nonlinearity in the distribution of the output current vectors necessary for realizing Boolean functionality by introducing three nonlinearity indicators. The analysis provides a physical interpretation of the effects of changing the hopping distance and temperature and is used in a comparison with data generated by a deep neural network trained on a physical device.

关键词： Carrier dynamics Dynamics of networks Electrical conductivity Fitness Hopping transport Network optimization Optimization problems Temperature

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Hybrid model for predicting an unknown process based on a cluster version of the K-nearest neighbors method

Hybrid model for predicting an unknown process based on a cl...

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Robotics, Artificial Intelligence and Intelligent Control (RAIIC), International Conference on

作者： Gatin Ruslan Novikova Svetlana Valitova Natalia Kremleva Elmira Applied Mathematics and Computer Science Department Kazan National Research Technical University named after A.N. Tupolev - KAI (KNRTU-KAI) Kazan Russia

ISBN: (数字)9798350352627

ISBN: (纸本)9798350352634

The authors have introduced a new model for predicting the values of an unknown process by combining clustering and k-nearest neighbor methods. One of the key features of this method is that it considers groups of similar vectors rather than individual vectors when determining the nearest neighbors of an unknown vector. When calculating distances between a vector and groups, a pairwise average is used to ensure that all available data is taken into account. Additionally, the method allows for the prediction of an unknown value using a separate model for each group. In the simplest scenario, the weighted average of known parameters within the closest cluster to the vector is proposed, with the weights being inversely proportional to the distance. The effectiveness of the developed model was tested by computational experiments using the example of predicting the level of metals in the blood of children and adolescents living in industrial centers. The forecast accuracy reaches 96% for small samples of 50 or fewer measurements. If the number of measurements is more than 350, then such data is no longer weakly determined, and the effectiveness of the hybrid model becomes less. Thus, the model is effective only for small samples.

关键词： Computational modeling Neural networks Process control Predictive models Nearest neighbor methods Prediction algorithms Vectors

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A spatiotemporal datamining approach for road profile estimation using low-cost device

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Procedia computer science 2022年 207卷 2767-2781页

作者： Mariem TAKTAK Slim TRIKI Higher Institute of Applied Sciences and Technologies of Sousse Department of Computer Science and Applied Mathematics Sousse Tunisia National Engineering School of Sfax Tunisia

In this paper, we address the problem of the spatiotemporal data mining in the field of the road conditions estimation. We demonstrate that road condition estimation can be defined as a problem of dynamic discovering of different classes of road profile during training phase. We focus on the road profile classification from time series data collected by a low-cost inertial sensor embedded into smartphone devices. Data used in this work are composed of three datasets which treat real asphalt pavement problems. The first, called Asphalt-obstacle, address the identification problem of obstacles in the pavement where data are collected. The second, called Asphalt pavement-type, aims at identifying three types of pavements namely flexible pavement, cobblestone street and dirt roads. The third and final dataset, called Asphalt pavement-regularity, treat the pavement quality effect on the driver comfortability. In order to estimate the road conditions, we conduct an evaluation of four spatiotemporal algorithms which combine concepts from machine learning and data-driven. With uniform parameters setting and on-line implementation property, we find that Markov spatiotemporal dynamic model achieve the best average classification accuracy of 90.5%±0.01 in 4-class Asphalt obstacles, 84%±0.006 in 3-class Asphalt pavement-type and 94.9%±0.005 in 2-class Asphalt pavement-regularity.

关键词： temporal data mining smartphone asphalt pavement classification

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