Pub Date : 2024-01-19DOI: 10.3389/fcomp.2024.1358454
Emma Frid, Kjetil Falkenberg, Kat R. Agres, Alex Lucas
{"title":"Editorial: New advances and novel applications of music technologies for health, well-being, and inclusion","authors":"Emma Frid, Kjetil Falkenberg, Kat R. Agres, Alex Lucas","doi":"10.3389/fcomp.2024.1358454","DOIUrl":"https://doi.org/10.3389/fcomp.2024.1358454","url":null,"abstract":"","PeriodicalId":52823,"journal":{"name":"Frontiers in Computer Science","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139611982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-18DOI: 10.3389/fcomp.2023.1252295
Mohamed Elkholy, Marwa A. Marzouk
Deep learning shows promising results in extracting useful information from medical images. The proposed work applies a Convolutional Neural Network (CNN) on retinal images to extract features that allow early detection of ophthalmic diseases. Early disease diagnosis is critical to retinal treatment. Any damage that occurs to retinal tissues that cannot be recovered can result in permanent degradation or even complete loss of sight. The proposed deep-learning algorithm detects three different diseases from features extracted from Optical Coherence Tomography (OCT) images. The deep-learning algorithm uses CNN to classify OCT images into four categories. The four categories are Normal retina, Diabetic Macular Edema (DME), Choroidal Neovascular Membranes (CNM), and Age-related Macular Degeneration (AMD). The proposed work uses publicly available OCT retinal images as a dataset. The experimental results show significant enhancement in classification accuracy while detecting the features of the three listed diseases.
深度学习在从医学图像中提取有用信息方面取得了可喜的成果。拟议的工作将卷积神经网络（CNN）应用于视网膜图像，以提取可早期检测眼科疾病的特征。早期疾病诊断对视网膜治疗至关重要。视网膜组织受到任何无法恢复的损伤，都可能导致视力永久退化甚至完全丧失。所提出的深度学习算法可根据从光学相干断层扫描（OCT）图像中提取的特征检测三种不同的疾病。深度学习算法使用 CNN 将 OCT 图像分为四类。这四个类别分别是正常视网膜、糖尿病黄斑水肿（DME）、脉络膜新生血管膜（CNM）和年龄相关性黄斑变性（AMD）。建议的工作使用公开的 OCT 视网膜图像作为数据集。实验结果表明，在检测上述三种疾病的特征时，分类准确率有了显著提高。
{"title":"Deep learning-based classification of eye diseases using Convolutional Neural Network for OCT images","authors":"Mohamed Elkholy, Marwa A. Marzouk","doi":"10.3389/fcomp.2023.1252295","DOIUrl":"https://doi.org/10.3389/fcomp.2023.1252295","url":null,"abstract":"Deep learning shows promising results in extracting useful information from medical images. The proposed work applies a Convolutional Neural Network (CNN) on retinal images to extract features that allow early detection of ophthalmic diseases. Early disease diagnosis is critical to retinal treatment. Any damage that occurs to retinal tissues that cannot be recovered can result in permanent degradation or even complete loss of sight. The proposed deep-learning algorithm detects three different diseases from features extracted from Optical Coherence Tomography (OCT) images. The deep-learning algorithm uses CNN to classify OCT images into four categories. The four categories are Normal retina, Diabetic Macular Edema (DME), Choroidal Neovascular Membranes (CNM), and Age-related Macular Degeneration (AMD). The proposed work uses publicly available OCT retinal images as a dataset. The experimental results show significant enhancement in classification accuracy while detecting the features of the three listed diseases.","PeriodicalId":52823,"journal":{"name":"Frontiers in Computer Science","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139614202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.3389/fcomp.2023.1292515
Ablaikhan Kadyrov, Altay Ussenbayev, Dariyash Kurenkeyeva, Aruzhan S Abdrakhmanova, Yersyn Y. Mukhanbetkaliyev, Zhanat Adilbekov, Andres Perez, S. Abdrakhmanov
The Severe Acute Respiratory Syndrome Coronavirus Infectious Disease 2019 (SARS-COVID-19) pandemic has dramatically improved the attitude that society has toward educational opportunities that are administered online. In many cases, digital platforms were adapted and utilized without formal evaluation of the needs, constraints, and opportunities associated with their use. Here, the eight historical faculties of veterinary sciences of Kazakhstan were surveyed to gather data on the use of online technology for the discipline in the country and the limitations, opportunities, and challenges associated with its use. Results show that technological resources, institutional support, and faculty and instructors' attitudes are highly favorable for the implementation of online education programs consistently throughout the country. In contrast, students' motivations and skills are perceived as variable, although generally favorable, at different locations. The results here provide insights into the challenges and opportunities associated with using online technology for instruction in veterinary sciences in Kazakhstan, which will help create the foundations for implementing this type of program in the country and region.
{"title":"Challenges and opportunities for online education of veterinary sciences in Kazakhstan","authors":"Ablaikhan Kadyrov, Altay Ussenbayev, Dariyash Kurenkeyeva, Aruzhan S Abdrakhmanova, Yersyn Y. Mukhanbetkaliyev, Zhanat Adilbekov, Andres Perez, S. Abdrakhmanov","doi":"10.3389/fcomp.2023.1292515","DOIUrl":"https://doi.org/10.3389/fcomp.2023.1292515","url":null,"abstract":"The Severe Acute Respiratory Syndrome Coronavirus Infectious Disease 2019 (SARS-COVID-19) pandemic has dramatically improved the attitude that society has toward educational opportunities that are administered online. In many cases, digital platforms were adapted and utilized without formal evaluation of the needs, constraints, and opportunities associated with their use. Here, the eight historical faculties of veterinary sciences of Kazakhstan were surveyed to gather data on the use of online technology for the discipline in the country and the limitations, opportunities, and challenges associated with its use. Results show that technological resources, institutional support, and faculty and instructors' attitudes are highly favorable for the implementation of online education programs consistently throughout the country. In contrast, students' motivations and skills are perceived as variable, although generally favorable, at different locations. The results here provide insights into the challenges and opportunities associated with using online technology for instruction in veterinary sciences in Kazakhstan, which will help create the foundations for implementing this type of program in the country and region.","PeriodicalId":52823,"journal":{"name":"Frontiers in Computer Science","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139622604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-11DOI: 10.3389/fcomp.2023.1305800
Tarik Chakkour
In this study, we present a fast algorithm for the numerical solution of the heat equation. The heat equation models the heat diffusion over time and through a given region. We engage a finite difference method to solve this equation numerically. The performance of its parallel implementation is considered using Message Passing Interface (MPI), Compute Unified Device Architecture (CUDA), and time schemes, such as Forward Euler (FE) and Runge-Kutta (RK) methods. The originality of this study is research on parallel implementations of the fourth-order Runge-Kutta method (RK4) for sparse matrices on Graphics Processing Unit (GPU) architecture. The supreme proprietary framework for GPU computing is CUDA, provided by NVIDIA. We will show three metrics through this parallelization to compare the computing performance: time-to-solution, speed-up, and performance. The spectral method is investigated by utilizing the FFTW software library, based on the computation of the fast Fourier transforms (FFT) in parallel and distributed memory architectures. Our CUDA-based FFT, named CUFFT, is performed in platforms, which is a highly optimized FFTW implementation. We will give numerical tests to reveal that this method is up-and-coming for solving the heat equation. The final result demonstrates that CUDA has a significant advantage and performance since the computational cost is tiny compared with the MPI implementation. This vital performance gain is also achieved through careful attention of managing memory communication and access.
{"title":"Parallel computation to bidimensional heat equation using MPI/CUDA and FFTW package","authors":"Tarik Chakkour","doi":"10.3389/fcomp.2023.1305800","DOIUrl":"https://doi.org/10.3389/fcomp.2023.1305800","url":null,"abstract":"In this study, we present a fast algorithm for the numerical solution of the heat equation. The heat equation models the heat diffusion over time and through a given region. We engage a finite difference method to solve this equation numerically. The performance of its parallel implementation is considered using Message Passing Interface (MPI), Compute Unified Device Architecture (CUDA), and time schemes, such as Forward Euler (FE) and Runge-Kutta (RK) methods. The originality of this study is research on parallel implementations of the fourth-order Runge-Kutta method (RK4) for sparse matrices on Graphics Processing Unit (GPU) architecture. The supreme proprietary framework for GPU computing is CUDA, provided by NVIDIA. We will show three metrics through this parallelization to compare the computing performance: time-to-solution, speed-up, and performance. The spectral method is investigated by utilizing the FFTW software library, based on the computation of the fast Fourier transforms (FFT) in parallel and distributed memory architectures. Our CUDA-based FFT, named CUFFT, is performed in platforms, which is a highly optimized FFTW implementation. We will give numerical tests to reveal that this method is up-and-coming for solving the heat equation. The final result demonstrates that CUDA has a significant advantage and performance since the computational cost is tiny compared with the MPI implementation. This vital performance gain is also achieved through careful attention of managing memory communication and access.","PeriodicalId":52823,"journal":{"name":"Frontiers in Computer Science","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139534146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-11DOI: 10.3389/fcomp.2023.1254678
Sara Lenzi, Ginevra Terenghi, Damiano Meacci, Aitor Moreno Fernandez-de-Leceta, Paolo Ciuccarelli
There is a growing need for solutions that can improve the communication between anomaly detection algorithms and human operators. In the context of real-time monitoring of networked systems, it is crucial that new solutions do not increase the burden on an already overloaded visual channel. Sonification can be leveraged as a peripheral monitoring tool that complements current visualization systems. We conceptualized, designed, and prototyped Datascapes, a framework project that explores the potential of sound-based applications for the monitoring of cyber-attacks on AI-supported networked environments. Within Datascapes, two Design Actions were realized that applied sonification on the monitoring and detection of anomalies in (1) water distribution networks and (2) Internet networks. Two series of prototypes were implemented and evaluated in a real-world environment with eight experts in network management and cybersecurity. This paper presents experimental results on the use of sonification to disclose anomalous behavior and assess both its gravity and the location within the network. Furthermore, we define and present a design methodology and evaluation protocol that, albeit grounded in sonification for anomaly detection, can support designers in the definition, development, and validation of real-world sonification applications.
{"title":"The design of Datascapes: toward a design framework for sonification for anomaly detection in AI-supported networked environments","authors":"Sara Lenzi, Ginevra Terenghi, Damiano Meacci, Aitor Moreno Fernandez-de-Leceta, Paolo Ciuccarelli","doi":"10.3389/fcomp.2023.1254678","DOIUrl":"https://doi.org/10.3389/fcomp.2023.1254678","url":null,"abstract":"There is a growing need for solutions that can improve the communication between anomaly detection algorithms and human operators. In the context of real-time monitoring of networked systems, it is crucial that new solutions do not increase the burden on an already overloaded visual channel. Sonification can be leveraged as a peripheral monitoring tool that complements current visualization systems. We conceptualized, designed, and prototyped Datascapes, a framework project that explores the potential of sound-based applications for the monitoring of cyber-attacks on AI-supported networked environments. Within Datascapes, two Design Actions were realized that applied sonification on the monitoring and detection of anomalies in (1) water distribution networks and (2) Internet networks. Two series of prototypes were implemented and evaluated in a real-world environment with eight experts in network management and cybersecurity. This paper presents experimental results on the use of sonification to disclose anomalous behavior and assess both its gravity and the location within the network. Furthermore, we define and present a design methodology and evaluation protocol that, albeit grounded in sonification for anomaly detection, can support designers in the definition, development, and validation of real-world sonification applications.","PeriodicalId":52823,"journal":{"name":"Frontiers in Computer Science","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139625377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The adversarial risk of a machine learning model has been widely studied. Most previous studies assume that the data lie in the whole ambient space. We propose to take a new angle and take the manifold assumption into consideration. Assuming data lie in a manifold, we investigate two new types of adversarial risk, the normal adversarial risk due to perturbation along normal direction and the in-manifold adversarial risk due to perturbation within the manifold. We prove that the classic adversarial risk can be bounded from both sides using the normal and in-manifold adversarial risks. We also show a surprisingly pessimistic case that the standard adversarial risk can be non-zero even when both normal and in-manifold adversarial risks are zero. We finalize the study with empirical studies supporting our theoretical results. Our results suggest the possibility of improving the robustness of a classifier without sacrificing model accuracy, by only focusing on the normal adversarial risk.
{"title":"Manifold-driven decomposition for adversarial robustness","authors":"Wenjia Zhang, Yikai Zhang, Xiaoling Hu, Yi Yao, Mayank Goswami, Chao Chen, Dimitris Metaxas","doi":"10.3389/fcomp.2023.1274695","DOIUrl":"https://doi.org/10.3389/fcomp.2023.1274695","url":null,"abstract":"The adversarial risk of a machine learning model has been widely studied. Most previous studies assume that the data lie in the whole ambient space. We propose to take a new angle and take the manifold assumption into consideration. Assuming data lie in a manifold, we investigate two new types of adversarial risk, the normal adversarial risk due to perturbation along normal direction and the in-manifold adversarial risk due to perturbation within the manifold. We prove that the classic adversarial risk can be bounded from both sides using the normal and in-manifold adversarial risks. We also show a surprisingly pessimistic case that the standard adversarial risk can be non-zero even when both normal and in-manifold adversarial risks are zero. We finalize the study with empirical studies supporting our theoretical results. Our results suggest the possibility of improving the robustness of a classifier without sacrificing model accuracy, by only focusing on the normal adversarial risk.","PeriodicalId":52823,"journal":{"name":"Frontiers in Computer Science","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139626142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-11DOI: 10.3389/fcomp.2023.1285690
Radoslaw Niewiadomski, Fanny Larradet, G. Barresi, L. Mattos
This paper addresses the need for collecting and labeling affect-related data in ecological settings. Collecting the annotations in the wild is a very challenging task, which, however, is crucial for the creation of datasets and emotion recognition models. We propose a novel solution to collect and annotate such data: a questionnaire based on the appraisal theory, that is accessible through an open-source mobile application. Our approach exploits a commercially available wearable physiological sensor connected to a smartphone. The app detects potentially relevant events from the physiological data, and prompts the users to report their emotions using a novel questionnaire based on the Ortony, Clore, and Collins (OCC) Model. The questionnaire is designed to gather information about the appraisal process concerning the significant event. The app guides a user through the reporting process by posing a series of questions related to the event. As a result, the annotated data can be used, e.g., to develop emotion recognition models. In the paper, we analyze users' reports. To validate the questionnaire, we asked 22 individuals to use the app and the sensor for a week. The analysis of the collected annotations shed new light on self-assessment in terms of appraisals. We compared a proposed method with two commonly used methods for reporting affect-related events: (1) a two-dimensional model of valence and arousal, and (2) a forced-choice list of 22 labels. According to the results, appraisal-based reports largely corresponded to the self-reported values of arousal and valence, but they differed substantially from the labels provided with a forced-choice list. In the latter case, when using the forced-choice list, individuals primarily selected labels of basic emotions such as anger or joy. However, they reported a greater variety of emotional states when using appraisal theory for self-assessment of the same events. Thus, proposed approach aids participants to focus on potential causes of their states, facilitating more precise reporting. We also found that regardless of the reporting mode (mandatory vs. voluntary reporting), the ratio between positive and negative reports remained stable. The paper concludes with a list of guidelines to consider in future data collections using self-assessment.
{"title":"Self-assessment of affect-related events for physiological data collection in the wild based on appraisal theories","authors":"Radoslaw Niewiadomski, Fanny Larradet, G. Barresi, L. Mattos","doi":"10.3389/fcomp.2023.1285690","DOIUrl":"https://doi.org/10.3389/fcomp.2023.1285690","url":null,"abstract":"This paper addresses the need for collecting and labeling affect-related data in ecological settings. Collecting the annotations in the wild is a very challenging task, which, however, is crucial for the creation of datasets and emotion recognition models. We propose a novel solution to collect and annotate such data: a questionnaire based on the appraisal theory, that is accessible through an open-source mobile application. Our approach exploits a commercially available wearable physiological sensor connected to a smartphone. The app detects potentially relevant events from the physiological data, and prompts the users to report their emotions using a novel questionnaire based on the Ortony, Clore, and Collins (OCC) Model. The questionnaire is designed to gather information about the appraisal process concerning the significant event. The app guides a user through the reporting process by posing a series of questions related to the event. As a result, the annotated data can be used, e.g., to develop emotion recognition models. In the paper, we analyze users' reports. To validate the questionnaire, we asked 22 individuals to use the app and the sensor for a week. The analysis of the collected annotations shed new light on self-assessment in terms of appraisals. We compared a proposed method with two commonly used methods for reporting affect-related events: (1) a two-dimensional model of valence and arousal, and (2) a forced-choice list of 22 labels. According to the results, appraisal-based reports largely corresponded to the self-reported values of arousal and valence, but they differed substantially from the labels provided with a forced-choice list. In the latter case, when using the forced-choice list, individuals primarily selected labels of basic emotions such as anger or joy. However, they reported a greater variety of emotional states when using appraisal theory for self-assessment of the same events. Thus, proposed approach aids participants to focus on potential causes of their states, facilitating more precise reporting. We also found that regardless of the reporting mode (mandatory vs. voluntary reporting), the ratio between positive and negative reports remained stable. The paper concludes with a list of guidelines to consider in future data collections using self-assessment.","PeriodicalId":52823,"journal":{"name":"Frontiers in Computer Science","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139626873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.3389/fcomp.2023.1286860
Sridhar Tayur, Ananth Tenneti
In this mini-review, we introduce and summarize research from the Quantum Technologies Group (QTG) at Carnegie Mellon University related to computational experience with quantum annealing, performed in collaboration with several other institutions including IIT-Madras and NASA (QuAIL). We present a novel hybrid quantum-classical heuristic algorithm (GAMA, Graver Augmented Multi-seed Algorithm) for non-linear, integer optimization, and illustrate it on an application (in cancer genomics). We then present an algebraic geometry-based algorithm for embedding a problem onto a hardware that is not fully connected, along with a companion Integer Programming (IP) approach. Next, we discuss the performance of two photonic devices - the Temporal Multiplexed Ising Machine (TMIM) and the Spatial Photonic Ising Machine (SPIM) - on Max-Cut and Number Partitioning instances. We close with an outline of the current work.
{"title":"Quantum annealing research at CMU: algorithms, hardware, applications","authors":"Sridhar Tayur, Ananth Tenneti","doi":"10.3389/fcomp.2023.1286860","DOIUrl":"https://doi.org/10.3389/fcomp.2023.1286860","url":null,"abstract":"In this mini-review, we introduce and summarize research from the Quantum Technologies Group (QTG) at Carnegie Mellon University related to computational experience with quantum annealing, performed in collaboration with several other institutions including IIT-Madras and NASA (QuAIL). We present a novel hybrid quantum-classical heuristic algorithm (GAMA, Graver Augmented Multi-seed Algorithm) for non-linear, integer optimization, and illustrate it on an application (in cancer genomics). We then present an algebraic geometry-based algorithm for embedding a problem onto a hardware that is not fully connected, along with a companion Integer Programming (IP) approach. Next, we discuss the performance of two photonic devices - the Temporal Multiplexed Ising Machine (TMIM) and the Spatial Photonic Ising Machine (SPIM) - on Max-Cut and Number Partitioning instances. We close with an outline of the current work.","PeriodicalId":52823,"journal":{"name":"Frontiers in Computer Science","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.3389/fcomp.2023.1286657
Sai Sakunthala Guddanti, Apurva Padhye, Anil Prabhakar, Sridhar Tayur
Early diagnosis of pneumonia is crucial to increase the chances of survival and reduce the recovery time of the patient. Chest X-ray images, the most widely used method in practice, are challenging to classify. Our aim is to develop a machine learning tool that can accurately classify images as belonging to normal or infected individuals. A support vector machine (SVM) is attractive because binary classification can be represented as an optimization problem, in particular as a Quadratic Unconstrained Binary Optimization (QUBO) model, which, in turn, maps naturally to an Ising model, thereby making annealing—classical, quantum, and hybrid—an attractive approach to explore. In this study, we offer a comparison between different methods: (1) a classical state-of-the-art implementation of SVM (LibSVM); (2) solving SVM with a classical solver (Gurobi), with and without decomposition; (3) solving SVM with simulated annealing; (4) solving SVM with quantum annealing (D-Wave); and (5) solving SVM using Graver Augmented Multi-seed Algorithm (GAMA). GAMA is tried with several different numbers of Graver elements and a number of seeds using both simulating annealing and quantum annealing. We found that simulated annealing and GAMA (with simulated annealing) are comparable, provide accurate results quickly, competitive with LibSVM, and superior to Gurobi and quantum annealing.
{"title":"Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM)","authors":"Sai Sakunthala Guddanti, Apurva Padhye, Anil Prabhakar, Sridhar Tayur","doi":"10.3389/fcomp.2023.1286657","DOIUrl":"https://doi.org/10.3389/fcomp.2023.1286657","url":null,"abstract":"Early diagnosis of pneumonia is crucial to increase the chances of survival and reduce the recovery time of the patient. Chest X-ray images, the most widely used method in practice, are challenging to classify. Our aim is to develop a machine learning tool that can accurately classify images as belonging to normal or infected individuals. A support vector machine (SVM) is attractive because binary classification can be represented as an optimization problem, in particular as a Quadratic Unconstrained Binary Optimization (QUBO) model, which, in turn, maps naturally to an Ising model, thereby making annealing—classical, quantum, and hybrid—an attractive approach to explore. In this study, we offer a comparison between different methods: (1) a classical state-of-the-art implementation of SVM (LibSVM); (2) solving SVM with a classical solver (Gurobi), with and without decomposition; (3) solving SVM with simulated annealing; (4) solving SVM with quantum annealing (D-Wave); and (5) solving SVM using Graver Augmented Multi-seed Algorithm (GAMA). GAMA is tried with several different numbers of Graver elements and a number of seeds using both simulating annealing and quantum annealing. We found that simulated annealing and GAMA (with simulated annealing) are comparable, provide accurate results quickly, competitive with LibSVM, and superior to Gurobi and quantum annealing.","PeriodicalId":52823,"journal":{"name":"Frontiers in Computer Science","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139382846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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