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Evaluating the Impact of Different Quantum Kernels on the Classification Performance of Support Vector Machine Algorithm: A Medical Dataset Application

arXiv

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arXiv 2024年

作者： Akpinar, Emine Islam, Sardar M.N. Oduncuoglu, Murat Department of Physics Yildiz Technical University Istanbul Turkey Applied Informatics Program ISILC Victoria University Melbourne Australia

The support vector machine algorithm with a quantum kernel estimator (QSVM-Kernel), as a leading example of a quantum machine learning technique, has undergone significant advancements. Nevertheless, its integration with classical data presents unique challenges. While quantum computers primarily interact with data in quantum states, embedding classical data into quantum states using feature mapping techniques is essential for leveraging quantum algorithms. Despite the recognized importance of feature mapping, its specific impact on data classification outcomes remains largely unexplored. This study addresses this gap by comprehensively assessing the effects of various feature mapping methods on classification results, taking medical data analysis as a case study. In this study, the QSVM-Kernel method was applied to classification problems in two different and publicly available medical datasets, namely, the Wisconsin Breast Cancer (original) and The Cancer Genome Atlas (TCGA) Glioma datasets. In the QSVM-Kernel algorithm, quantum kernel matrices obtained from 9 different quantum feature maps were used. Thus, the effects of these quantum feature maps on the classification results of the QSVM-Kernel algorithm were examined in terms of both classifier performance and total execution time. As a result, in the Wisconsin Breast Cancer (original) and TCGA Glioma datasets, when Rx and Ry rotational gates were used, respectively, as feature maps in the QSVM-Kernel algorithm, the best classification performances were achieved both in terms of classification performance and total execution time. The contributions of this study are that (1) it highlights the significant impact of feature mapping techniques on medical data classification outcomes using the QSVM-Kernel algorithm, and (2) it also guides undertaking research for improved QSVM classification performance. © 2024, CC BY-NC-SA.

关键词： Classification (of information)

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A Comparison of Multi Class Support Vector Machine vs Deep Convolutional Neural Network for Brain Tumor Classification

A Comparison of Multi Class Support Vector Machine vs Deep C...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Cahyo, Nur Ryan Dwi Sari, Christy Atika Rachmawanto, Eko Hari Jatmoko, Cahaya Al-Jawry, Rabei Raad Ali Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Department of Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia Northern Technical University Department Computer Science Semarang Indonesia Northern Technical University Department of Computer Engineering Technology Mosul Iraq National University of Science and Technology Department of Computer Sciece DhiQar Iraq

ISBN: (纸本)9798350339215

Multi SVM has long been one of the popular methods in classification, while DCNN has recently gained significant attention in image processing and pattern recognition. This research evaluates the effectiveness of Multi Class Support Vector Machine (M-SVM) and Deep Convolutional Neural Network (DCNN) techniques in classifying brain tumors. A dataset of 2660 3D medical images with dimensions 227 x 227 x 3;including Glioma, Meningioma, and Pituitary tumors, has been partitioned into distinct sets for both training and testing purposes. DCNN approach achieves excellent accuracy in identifying tumor names, with a training accuracy of 97.8% and 100% success rate in 9 experiments. The Multi SVM method demonstrates relatively good accuracy, with training accuracies ranging from 70% to 90% based on different kernel functions. These findings provide valuable insights for selecting appropriate methods in brain tumor classification and encourage further exploration of hybrid Multi SVM-DCNN approaches to enhance accuracy and reliability. © 2023 IEEE.

关键词： Convolutional neural networks

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PWM Signal Reconstruction to Operate the iBooster Brake for Electric Vehicle: A Reverse Engineering 1

PWM Signal Reconstruction to Operate the iBooster Brake for ...

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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Haq, Muhamad Fajar Imanul Latifah, Faiza Pratama, Mulia Putri, Dewi Indriati Hadi Wahid, Muhammad Rizalul Wahono, Bambang Praptijanto, Achmad Putrasari, Yanuandri Dimyani, Ahmad Nur, Arifin Wardana, Muhammad Khristamto Aditya Suherman Program Study of Mechatronics and Artificial Intelligence Universitas Pendidikan Indonesia Purwakarta Indonesia Research Organization of Electronics and Informatics National Research and Innovation Agency Research Center for Smart Mechatronics Bandung Indonesia Mechatronics and Artificial Intelligence Universitas Pendidikan Indonesia Purwakarta Indonesia

ISBN: (纸本)9798350368918

The rise of Advanced Driver Assistance Systems (ADAS) has emphasized the need for technologies such as Automatic Emergency Braking (AEB) and Adaptive Cruise Control, both of which require brake activation without driver input. Since 2016, automotive manufacturers, particularly in the U.S., have been incorporating AEB technologies in response to safety demands. Bosch's iBooster, an electromechanical brake booster, has gained prominence for its role in autopilot and braking systems, notably in Tesla's Model S and X. The iBooster system can generate full braking pressure in just 120 milliseconds and allows for electronic control while still maintaining manual override capabilities. Tesla's iBooster Gen1, used in its autopilot system, operates via a Controlled Area Network (CAN), though the exact method is not publicly available. Honda has also adopted the iBooster Gen1 in the 2018 CR-V, utilizing it for the Brake Assist feature, although details on Honda's CAN network usage remain limited. This paper explores reverse engineering of the iBooster to uncover key hardware and software technologies. The reverse engineering operation entails altering the system's control loop, capturing the pedal sensor signal, and replicating this signal using an Arduino. Additionally, it includes designing circuit schematics and PCBs. The results demonstrate the success of this reverse engineering approach, which uses a work cycle and Pulse Width Modulation (PWM) to interact with the iBooster Gen1. The method produced identical responses to the iBooster with PWM values set at 30%, 40%, and 50%. © 2024 IEEE.

关键词： Advanced driver assistance systems

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High Capacity Information Hiding Method Based on Interpolation and Ones' complement

High Capacity Information Hiding Method Based on Interpolati...

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2022 IET International Conference on Engineering Technologies and Applications, IET-ICETA 2022

作者： Lin, Cheng-Yi Shongwe, Njabulo Huang, Cheng-Ta Yuan Ze University International Bachelor Program in Informatics Taoyuan City Taiwan Yuan Ze University Department of Information Management Taoyuan City Taiwan

ISBN: (数字)9781665491389

ISBN: (纸本)9781665491389

Data hiding is an important technology for privacy protection. The proposed information hiding method is based on neighborhood mean interpolation (NMI) and 1' s complement. Different from other previous schemes, the proposed method can embed at least one bit of secret data in each pixel. Also, in some cases, secret data is converted to its one's complement. This makes the hiding ability better than other schemes. Experimental results comparing the proposed interpolation method with other methods show that the proposed method achieves more than 20% improvement in hiding ability and 8% improvement in PSNR. © 2022 IEEE.

关键词： Interpolation

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Safe Breast Cancer Diagnosis Resilient to Mammographic Adversarial Samples 1st

Safe Breast Cancer Diagnosis Resilient to Mammographic Adve...

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1st Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024

作者： Hao, Degan Arefan, Dooman Zuley, Margarita L. Berg, Wendie A. Wu, Shandong Intelligent Systems Program University of Pittsburgh PittsburghPA United States Department of Radiology University of Pittsburgh PittsburghPA United States Department of Bioengineering University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States

ISBN: (纸本)9783031777882

Adversarial data can lead to malfunction of deep learning applications. It is essential to develop deep learning models that are resilient to adversarial data while accurate on standard, clean data. In this study, we focus on building safe breast cancer diagnosis models against mammographic adversarial samples. We proposed a novel adversarially robust feature learning (ARFL) method to facilitate adversarial training using both standard data and adversarial data, where a feature correlation measure is incorporated as an objective function to encourage learning of robust features and restrain spurious features. To show the efficacy of ARFL for robust breast cancer diagnosis, we built and evaluated deep learning diagnosis models using two independent clinically collected breast imaging datasets, comprising a total of 9,548 mammogram images. We performed extensive experiments showing that the ARFL method outperformed several state-of-the-art methods. ARFL can serve as an effective method to enhance adversarial training, towards building safe breast cancer diagnosis against adversarial attacks in clinical settings. The code repository of this study is publicly available at GitHub: https://***/usernamesafeai/ARFL. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Sentiment Analysis of Vaccine Perceptions in Indonesia: A Comparative Study Using the Naïve Bayes Multinomial Text Algorithm on Twitter Data 25

Sentiment Analysis of Vaccine Perceptions in Indonesia: A Co...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Hermawan, Dedy Aditya, Addin Purwiantono, Febry Eka Sekolah Tinggi Informatika & Komputer Indonesia Dept. of Informatics Malang Indonesia Sekolah Tinggi Informatika & Komputer Indonesia Dept. of Information System Malang Indonesia Sekolah Tinggi Informatika & Komputer Indonesia Diploma Program of Information System Malang Indonesia

ISBN: (纸本)9798350378573

In the ongoing COVID-19 situation, vaccines have played a main role in mitigating the propagation of the virus and reducing its impact on public health. In Indonesia, various vaccines have been deployed, including Sinovac, AstraZeneca, Pfizer, and Moderna. Understanding public perception of these vaccines is vital for addressing vaccine hesitancy and ensuring widespread immunization coverage. Public sentiment can significantly influence vaccination uptake, making it essential to analyze and understand these perceptions accurately. This study focused on analyzing public perception regarding the use of the Sinovac, AstraZeneca, Pfizer, and Moderna vaccines in Indonesia. Classifying tweets using The Naïve Bayes algorithm is help to compare either positive or negative sentiments. Data collection is conducted using the Twitter API (Application programming Interface). The data analysis process involves grouping data and assigning labels, followed by preprocessing steps including case folding, tokenizing, filtering tokens by length, removing stop words, and stemming. The Naïve Bayes Multinomial Text algorithm is chosen for its capability to classify documents based on word occurrence frequencies. Results from the processed data show the accuracy rates for the Sinovac vaccine category with a dataset of 5000 tweets at 95%, AstraZeneca vaccine with a dataset of 1500 tweets at 98.01%, Pfizer vaccine with a dataset of 1500 tweets at 95.75%, and Moderna vaccine with a dataset of 120 tweets at 96.88%. The analysis concludes that the public perception of these vaccines is generally positive. © 2024 IEEE.

关键词： Tweets

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Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space 38

Emergence of Hidden Capabilities: Exploring Learning Dynamic...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Park, Core Francisco Okawa, Maya Lee, Andrew Tanaka, Hidenori Lubana, Ekdeep Singh CBS-NTT Program in Physics of Intelligence Harvard University United States Department of Physics Harvard University United States Physics & Informatics Laboratories NTT Research Inc. United States EECS Department University of Michigan Ann Arbor United States

Modern generative models demonstrate impressive capabilities, likely stemming from an ability to identify and manipulate abstract concepts underlying their training data. However, fundamental questions remain: what determines the concepts a model learns, the order in which it learns them, and its ability to manipulate those concepts? To address these questions, we propose analyzing a model's learning dynamics via a framework we call the concept space, where each axis represents an independent concept underlying the data generating process. By characterizing learning dynamics in this space, we identify how the speed at which a concept is learned, and hence the order of concept learning, is controlled by properties of the data we term concept signal. Further, we observe moments of sudden turns in the direction of a model's learning dynamics in concept space. Surprisingly, these points precisely correspond to the emergence of hidden capabilities, i.e., where latent interventions show the model possesses the capability to manipulate a concept, but these capabilities cannot yet be elicited via naive input prompting. While our results focus on synthetically defined toy datasets, we hypothesize a general claim on emergence of hidden capabilities may hold: generative models possess latent capabilities that emerge suddenly and consistently during training, though a model might not exhibit these capabilities under naive input prompting. © 2024 Neural information processing systems foundation. All rights reserved.

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Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult congenital heart disease in the USA: a multicentre modelling study

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The Lancet Digital Health 2025年第4期7卷 e264-e274页

作者： Mayourian, Joshua Asztalos, Ivor B El-Bokl, Amr Lukyanenko, Platon Kobayashi, Ryan L La Cava, William G Ghelani, Sunil J Vetter, Victoria L Triedman, John K Department of Cardiology Boston Children's Hospital Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children's Hospital Department of Pediatrics Harvard Medical School BostonMA United States Division of Pediatric Cardiology Perelman School of Medicine at the University of Pennsylvania Children's Hospital of Philadelphia PhiladelphiaPA United States

Background: Left ventricular systolic dysfunction (LVSD) is independently associated with cardiovascular events in patients with congenital heart disease. Although artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis is predictive of LVSD in the general adult population, it has yet to be applied comprehensively across congenital heart disease lesions. Methods: We trained a convolutional neural network on paired ECG–echocardiograms (≤2 days apart) across the lifespan of a wide range of congenital heart disease lesions to detect left ventricular ejection fraction (LVEF) of 40% or less. Model performance was evaluated on single ECG–echocardiogram pairs per patient at Boston Children's Hospital (Boston, MA, USA) and externally at the Children's Hospital of Philadelphia (Philadelphia, PA, USA) using area under the receiver operating (AUROC) and precision-recall (AUPRC) curves. Findings: The training cohort comprised 124 265 ECG–echocardiogram pairs (49 158 patients;median age 10·5 years [IQR 3·5–16·8];3381 [2·7%] of 124 265 ECG–echocardiogram pairs with LVEF ≤40%). Test groups included internal testing (21 068 patients;median age 10·9 years [IQR 3·7–17·0];3381 [2·7%] of 124 265 ECG–echocardiogram pairs with LVEF ≤40%) and external validation (42 984 patients;median age 10·8 years [IQR 4·9–15·0];1313 [1·7%] of 76 400 ECG–echocardiogram pairs with LVEF ≤40%) cohorts. High model performance was achieved during internal testing (AUROC 0·95, AUPRC 0·33) and external validation (AUROC 0·96, AUPRC 0·25) for a wide range of congenital heart disease lesions. Patients with LVEF greater than 40% by echocardiogram who were deemed high risk by AI-ECG were more likely to have future dysfunction compared with low-risk patients (hazard ratio 12·1 [95% CI 8·4–17·3];p © 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license

关键词： Convolutional neural networks

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Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis 38

Disentangling Interpretable Factors with Supervised Independ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Su, Jiayu Knowles, David A. Rabadan, Raul Program for Mathematical Genomics United States Department of Systems Biology Columbia University United States Department of Biomedical Informatics Columbia University United States Department of Computer Science Columbia University United States New York Genome Center United States

The success of machine learning models relies heavily on effectively representing high-dimensional data. However, ensuring data representations capture human-understandable concepts remains difficult, often requiring the incorporation of prior knowledge and decomposition of data into multiple subspaces. Traditional linear methods fall short in modeling more than one space, while more expressive deep learning approaches lack interpretability. Here, we introduce Supervised Independent Subspace Principal Component Analysis (sisPCA), a PCA extension designed for multi-subspace learning. Leveraging the Hilbert-Schmidt Independence Criterion (HSIC), sisPCA incorporates supervision and simultaneously ensures subspace disentanglement. We demonstrate sisPCA's connections with autoencoders and regularized linear regression and showcase its ability to identify and separate hidden data structures through extensive applications, including breast cancer diagnosis from image features, learning aging-associated DNA methylation changes, and single-cell analysis of malaria infection. Our results reveal distinct functional pathways associated with malaria colonization, underscoring the essentiality of explainable representation in high-dimensional data analysis. © 2024 Neural information processing systems foundation. All rights reserved.

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Relation of Determinant Factors and Their Effect on Improving the Online Learning : A systematic literature review 5

Relation of Determinant Factors and Their Effect on Improvin...

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5th International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023

作者： Widiartha, Ida Bagus Ketut Hwang, Junseok Nugraha, Ryan Adhitya University of Mataram Dept. of Informatics Engineering Mataram Indonesia Seoul National University Technology Management Economic Policy Program Seoul Korea Republic of Telkom University Dept. of Information System Bandung Indonesia

ISBN: (纸本)9798350303414

This study introduces the Harmony Model, which examines factors influencing the success of online learning and the workload of lecturers. Drawing upon the Theory of Action Reason, it explores the impact of student satisfaction and lecturers' willingness to prepare course materials. The model emphasizes the challenge faced by lecturers in creating high-quality content and highlights the importance of balancing supporting factors and alleviating the associated burden. Strategies to enhance online learning effectiveness should address both lecturers' motivation and student satisfaction. Identifying determinant factors and analyzing their impact on material development can increase lecturers' willingness to provide appropriate course materials. Knowledge extraction processes and satisfaction-related factors play crucial roles. This study offers insights to enhance the online learning experience, optimize learning outcomes, and improve overall effectiveness. © 2023 IEEE.

关键词： Curricula

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