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Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

Computer systems science & Engineering 2023年第2期44卷 1793-1806页

作者： G.Ignisha Rajathi R.Ramesh Kumar D.Ravikumar T.Joel Seifedine Kadry Chang-Won Jeong Yunyoung Nam Department of Computer Science and Business Systems Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641008India Department of Electronics and Communication Engineering Kings Engineering CollegeChennai602117India Department of Electronics and Communication Engineering R.M.K.Engineering CollegeChennai601206India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,Internet of Medical Things(IoMT)has gained considerable attention to provide improved healthcare services to *** earlier diag-nosis of brain tumor(BT)using medical imaging becomes an essential task,auto-mated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning *** this motivation,this paper introduces a novel IoMT and cloud enabled BT diagnosis model,named *** IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging(MRI)brain images and transmit them to the cloud ***,adaptive windowﬁltering(AWF)based image preprocessing is used to remove *** addition,the cloud server executes the disease diagnosis model which includes the sparrow search algorithm(SSA)with GoogleNet(SSA-GN)*** IoMTC-HDBT model applies functional link neural network(FLNN),which has the ability to detect and classify the MRI brain images as normal or ***ﬁnds useful to generate the reports instantly for patients located in remote *** validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is car-ried out interms of sensitivity,accuracy,and speciﬁ*** experimentation out-come pointed out the betterment of the proposed model with the accuracy of 0.984.

关键词： Internet of medical things healthcare brain tumor disease classiﬁcation deep learning metaheuristics

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Towards understanding structure–property relations in materials with interpretable deep learning

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npj Computational Materials 2023年第1期9卷 157-168页

作者： Tien-Sinh Vu Minh-Quyet Ha Duong-Nguyen Nguyen Viet-Cuong Nguyen Yukihiro Abe Truyen Tran Huan Tran Hiori Kino Takashi Miyake Koji Tsuda Hieu-Chi Dam Japan Advanced Institute of Science and Technology NomiIshikawaJapan HPC SYSTEMS Inc. MinatoTokyoJapan Applied Artificial Intelligence Institute Deakin UniversityGeelongVICAustralia School of Materials Science and Engineering Georgia Institute of TechnologyAtlantaGAUSA Research and Services Division of Materials Data and Integrated System National Institute for Materials ScienceTsukubaIbarakiJapan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and TechnologyTsukubaIbarakiJapan Department of Computational Biology and Medical Sciences Graduate School of Frontier SciencesUniversity of TokyoKashiwa-shiChiba-kenJapan International Center for Synchrotron Radiation Innovation Smart(SRIS) Tohoku UniversityAoba-kuSendaiJapan

Deep learning(DL)models currently employed in materials research exhibit certain limitations in delivering meaningful information for interpreting predictions and comprehending the relationships between structure and material *** address these limitations,we propose an interpretable DL architecture that incorporates the attention mechanism to predict material properties and gain insights into their structure–property *** proposed architecture is evaluated using two well-known datasets(the QM9 and the Materials Project datasets),and three in-house-developed computational materials ***–test–split validations confirm that the models derived using the proposed DL architecture exhibit strong predictive capabilities,which are comparable to those of current state-of-the-art ***,comparative validations,based on first-principles calculations,indicate that the degree of attention of the atoms’local structures to the representation of the material structure is critical when interpreting structure–property relationships with respect to physical *** properties encompass molecular orbital energies and the formation energies of *** proposed architecture shows great potential in accelerating material design by predicting material properties and explicitly identifying crucial features within the corresponding structures.

关键词： properties property structure

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Identifying Critical Tokens for Accurate Predictions in Transformer-based Medical Imaging Models

arXiv

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arXiv 2025年

作者： Kang, Solha Vankerschaver, Joris Ozbulak, Utku Center for Biosystems and Biotech Data Science Ghent University Global Campus Korea Republic of Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium Department of Electronics and Information Systems Ghent University Belgium

With the advancements in self-supervised learning (SSL), transformer-based computer vision models have recently demonstrated superior results compared to convolutional neural networks (CNNs) and are poised to dominate the field of artificial intelligence (AI)-based medical imaging in the upcoming years. Nevertheless, similar to CNNs, unveiling the decision-making process of transformer-based models remains a challenge. In this work, we take a step towards demystifying the decision-making process of transformer-based medical imaging models and propose "Token Insight", a novel method that identifies the critical tokens that contribute to the prediction made by the model. Our method relies on the principled approach of token discarding native to transformer-based models, requires no additional module, and can be applied to any transformer model. Using the proposed approach, we quantify the importance of each token based on its contribution to the prediction and enable a more nuanced understanding of the model’s decisions. Our experimental results which are showcased on the problem of colonic polyp identification using both supervised and self-supervised pretrained vision transformers indicate that Token Insight contributes to a more transparent and interpretable transformer-based medical imaging model, fostering trust and facilitating broader adoption in clinical settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar datasets?

arXiv

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arXiv 2025年

作者： Ozbulak, Utku Anzaku, Esla Timothy Kang, Solha De Neve, Wesley Vankerschaver, Joris Center for Biosystems and Biotech Data Science Ghent University Global Campus Korea Republic of Department of Electronics and Information Systems Ghent University Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

Machine learning (ML) research strongly relies on benchmarks in order to determine the relative effectiveness of newly proposed models. Recently, a number of prominent research effort argued that a number of models that improve the state-of-the-art by a small margin tend to do so by winning what they call a "benchmark lottery". An important benchmark in the field of machine learning and computer vision is the ImageNet where newly proposed models are often showcased based on their performance on this dataset. Given the large number of self-supervised learning (SSL) frameworks that has been proposed in the past couple of years each coming with marginal improvements on the ImageNet dataset, in this work, we evaluate whether those marginal improvements on ImageNet translate to improvements on similar datasets or not. To do so, we investigate twelve popular SSL frameworks on five ImageNet variants and discover that models that seem to perform well on ImageNet may experience significant performance declines on similar datasets. Specifically, state-of-the-art frameworks such as DINO and Swav, which are praised for their performance, exhibit substantial drops in performance while MoCo and Barlow Twins displays comparatively good results. As a result, we argue that otherwise good and desirable properties of models remain hidden when benchmarking is only performed on the ImageNet validation set, making us call for more adequate benchmarking. To avoid the "benchmark lottery" on ImageNet and to ensure a fair benchmarking process, we investigate the usage of a unified metric that takes into account the performance of models on other ImageNet variant datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Unraveling the early universe’s equation of state and primordial black hole production with PTA,BBN,and CMB observations

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Chinese Physics C 2024年第12期48卷 212-220页

作者： Qing-Hua Zhu Zhi-Chao Zhao Sai Wang Xin Zhang Department of Physics and Chongqing Key Laboratory for Strongly Coupled Physics Chongqing UniversityChongqing 401331China Department of Applied Physics College of ScienceChina Agricultural UniversityQinghua East RoadBeijing 100083China Theoretical Physics Division Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China

Pulsar timing array(PTA)data releases show strong evidence for a stochastic gravitational-wave background in the nanohertz *** the signal is interpreted by a scenario of scalar-induced gravitational waves(SIGWs),we encounter overproduction of primordial black holes(PBHs).We wonder if varying the equation of state(EoS)of the early Universe can resolve this issue and thereby lead to a consistent interpretation of the PTA *** a data combination of PTA,big-bang nucleosynthesis,and cosmic microwave background,we find that an epoch with EoS w~O(10^(-2))between the end of inflation and the onset of radiation domination can significantly suppress the production of PBHs,leading to alleviation of the PBH-overproduction *** the inferred interval w=0.44_(-0.40)^(+0.52)at 95%confidence level,our scenario can interpret the PTA data just as well as the conventional scenario of SIGWs produced during the radiation domination.

关键词： physics of the early universe gravitational waves primordial black hole pulsar timing arrays

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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar datasets?

Self-supervised Benchmark Lottery on ImageNet: Do Marginal I...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Utku Ozbulak Esla Timothy Anzaku Solha Kang Wesley De Neve Joris Vankerschaver Center for Biosystems and Biotech Data Science Ghent University Global Campus Republic of Korea Department of Electronics and Information Systems Ghent University Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

关键词： Measurement Computer vision Computational modeling Neural networks Machine learning Self-supervised learning Benchmark testing

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CONVERGENCE ANALYSIS OF THE TRANSFORMED GRADIENT PROJECTION ALGORITHMS ON COMPACT MATRIX MANIFOLDS

arXiv

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arXiv 2024年

作者： Ding, Wentao Li, Jianze Zhang, Shuzhong School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen China Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States

In this paper, to address the optimization problem on a compact matrix manifold, we introduce a novel algorithmic framework called the Transformed Gradient Projection (TGP) algorithm, using the projection onto this compact matrix manifold. Compared with the existing algorithms, the key innovation in our approach lies in the utilization of a new class of search directions and various stepsizes, including the Armijo, nonmonotone Armijo, and fixed stepsizes, to guide the selection of the next iterate. Our framework offers flexibility by encompassing the classical gradient projection algorithms as special cases, and intersecting the retraction-based line-search algorithms. Notably, our focus is on the Stiefel or Grassmann manifold, revealing that many existing algorithms in the literature can be seen as specific instances within our proposed framework, and this algorithmic framework also induces several new special cases. Then, we conduct a thorough exploration of the convergence properties of these algorithms, considering various search directions and stepsizes. To achieve this, we extensively analyze the geometric properties of the projection onto compact matrix manifolds, allowing us to extend classical inequalities related to retractions from the literature. Building upon these insights, we establish the weak convergence, convergence rate, and global convergence of TGP algorithms under three distinct stepsizes. In cases where the compact matrix manifold is the Stiefel or Grassmann manifold, our convergence results either encompass or surpass those found in the literature. Finally, through a series of numerical experiments, we observe that the TGP algorithms, owing to their increased flexibility in choosing search directions, outperform classical gradient projection and retraction-based line-search algorithms in several *** Codes 15A23, 49M37, 65K05, 90C26, 90C30 Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

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Intra-datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinola, Damola Gideon Adetiba, Emmanuel Abayomi, Abdultaofeek Nnaji, Uche Thakur, Surendra Moyo, Sibusiso Covenant University Covenant Applied Informatics and Communications African Center of Excellence Department of Electrical and Information Engineering Ota Nigeria Durban University of Technology HRA Institute for Systems Science Durban South Africa Mangosuthu University of Technology Department of Information and Communication Technology Durban South Africa Covenant University Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Ota Nigeria Durban University of Technology KZN E-Skills CoLab Durban South Africa Stellenbosch University School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358155

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users' requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named;the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment. © 2024 IEEE.

关键词： Cloud platforms

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Discrimination between Cynomolgus Monkey (Macaca Fascicularis) and Humans using Machine Learning and Heart Rate Variability Indices

Discrimination between Cynomolgus Monkey (Macaca Fasciculari...

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2022 World Automation Congress, WAC 2022

作者： Kaneko, Itaru Hirahara, Daisuke Hayano, Junichiro Mozos, Oscar Martinez Yuda, Emi Center for Data-driven Science and Artificial Intelligence Tohoku University Sendai Japan Department of Health Sciences Graduate School of Medicine Tohoku University Sendai Japan Center for Applied Autonomous Sensor Systems ÖRebro University Örebro Sweden Nagoya City University Nagoya Japan

ISBN: (数字)9798888314449

ISBN: (纸本)9798888314449

In recent years, from the viewpoint of privacy protection policy, research on the personal identification of human bio-signals has been prominent. However, it is not well known whether indices obtained from biological time series data, such as ECGs, have personal identifiability, and it is not clear and can they be discriminated from animal ECGs. In this study, we visualized Heart Rate Variability (HRV) indices data of a cynomolgus monkey (Macaca Fascicularis) and a newborn using T-SNE, which is often used for data dimensionality compression in unsupervised learning, and UMAP, which is a new method. As a result, it was difficult to discriminate between cynomolgus monkey and newborn HRV indices. © 2022 TSI Enterprises.

关键词： Electrocardiograms

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Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories

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Procedia CIRP 2024年 128卷 424-429页

作者： Julian Weller Nico Migenda Sebastian von Enzberg Martin Kohlhase Wolfram Schenck Roman Dumitrescu Fraunhofer Institute for Mechatronic Systems Design Zukunftsmeile 1 33102 Paderborn Germany Center for Applied Data Science Gütersloh Faculty of Engineering and Mathematics Bielefeld University of Applied Sciences and Arts Schulstraße 10 33330 Gütersloh Germany Chair for Advanced Systems Engineering Heinz Nixdorf Institute Fürstenallee 11 33102 Paderborn Germany

The emerging trend of Prescriptive Analytics tries to prescribe actionable decisions. The goal is to help production experts in automating their decisions in the factory. Decision making processes in factories mostly rely on experts inside the company. Current trends such as demographic shifts and shortages of skilled personnel are challenging organizations to find ways to become less dependent on the ad hoc availability of expert knowledge through digitalization and data analytics. Existing data analytics approaches for smart factories usually focus on description, diagnosis, and prediction. An approach to differentiate between different application areas of Prescriptive Analytics in a smart factory is presented. Focus areas for a useful application and integration of Prescriptive Analytics are derived. Based on these applications, design principles are constructed. They help decision makers to select future lighthouse use cases for development and help smart factory advocates to strategically decide which prescriptive analytics approaches to highlight. The resulting design principles are based on a literature review and validated in a workshop with experts from research. Approaches are differentiated and key characteristics for future Use Case design decisions are derived.

关键词： Prescriptive Analytics Smart Factory Design methodology tools technologies

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