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7th International Conference on Parallel, Distributed and Grid Computing, PDGC 2022

作者： Saxena, Harshit Joshi, Divyansh Singh, Hari Anand, Rohit Jaypee University of Information Technology Computer Science and Engineering Department Himachal Pradesh Solan India G.B.Pant Dseu Okhla-1 Campus Ece Department New Delhi India

ISBN: (纸本)9781665454018

The application of machine learning to investigate metabolic disorders like diabetes and Alzheimer's Disease (AD), which impact significant populations of people worldwide, is currently receiving a lot of attention. Early on, AD was challenging to predict. The method of identifying the parameters that were most effective for predicting Alzheimer's disease involved the use of decision trees, support vector machines, XGBoost, random forest, extra trees, AdaBoost, gradient boosting, and voting classifiers. In this research, the Extra Trees classifier has been used which, from our research, has rarely been explored in the past. This classifier gives the best results in terms of performance among all the implemented techniques. The performance is evaluated using evaluation parameters such as precision, recall, F1-Score and accuracy. Using these machine learning methods, especially the Extra Trees algorithm, for early identification of Alzheimer's disease will greatly benefit in lowering the annual mortality rate caused by AD. © 2022 IEEE.

关键词： Adaptive boosting

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An integrated microfluidic platform for nucleic acid testing

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Microsystems & Nanoengineering 2024年第3期10卷 181-194页

作者： Antao Sun Petra Vopařilová Xiaocheng Liu Bingqian Kou TomášŘezníček TomášLednický Sheng Ni JiříKudr Ondřej Zítka Zdenka Fohlerová Petr Pajer Haoqing Zhang Pavel Neužil Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace School of Mechanical EngineeringNorthwestern Polytechnical University127 West Youyi RoadXi’anShaanxi 710072P.R.China Department of Chemistry and Biochemistry Mendel University in BrnoZemědělská161300 BrnoCzech Republic ITD Tech s.r.o Osvoboditelů100573581 BohumínCzech Republic Central European Institute of Technology Brno University of TechnologyPurkyňova 123Brno 61200Czech Republic Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong Kong SARChina Department of Microelectronics Faculty of Electrical Engineering and CommunicationBrno University of TechnologyTechnická3058/10Brno 61600Czech Republic Military Health Institute U Vojenskénemocnice 120016200 Praha 6Czech Republic The Key Laboratory of Biomedical Information Engineering of Ministry of Education School of Life Science and TechnologyXi’an Jiaotong UniversityXi’anShaanxi 710049P.R.China Bioinspired Engineering and Biomechanics Center(BEBC) Xi’an Jiaotong UniversityXi’an 710049P.R.China

This study presents a rapid and versatile low-cost sample-to-answer system for SARS-CoV-2 *** system integrates the extraction and purification of nucleic acids,followed by amplification via either reverse transcriptionquantitative polymerase chain reaction(RT–qPCR)or reverse transcription loop-mediated isothermal amplification(RT–LAMP).By meeting diverse diagnostic and reagent needs,the platform yields testing results that closely align with those of commercial RT-LAMP and RT‒qPCR *** advantages of our system include its speed and *** assay is completed within 28 min,including sample loading(5 min),ribonucleic acid(RNA)extraction(3 min),and RT-LAMP(20 min).The cost of each assay is≈$9.5,and this pricing is competitive against that of Food and Drug Administration(FDA)-approved commercial *** some RNA loss during on-chip extraction is observed,the platform maintains a potential limit of detection lower than 297 *** makes the system particularly useful in environments where centralized laboratories are either unavailable or inconveniently *** key feature is the platform’s versatility,allowing users to choose between RT‒qPCR or RT‒LAMP tests based on specific requirements.

关键词： testing maintain versatile

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Learning Shape-Preserving Autoencoder for the Reconstruction of Functional Data from Noisy Observations 23rd

Learning Shape-Preserving Autoencoder for the Reconstructi...

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23rd International Conference on Computational Science, ICCS 2023

作者： Krzyżak, Adam Rafajlowicz, Wojciech Rafajlowicz, Ewaryst Department of Computer Science and Software Engineering Concordia University 1455 De Maisonneuve Blvd. West MontrealQCH3G 1M8 Canada Szczecin Poland Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783031360206

We propose a new autoencoder preserving input functions’ general shape (monotonicity, convexity) after their reconstruction without imposing a priori constraints. These properties are inherent to the coefficients of the Bernstein-Durrmeyer polynomials that serve here as theoretical descriptors. Their estimates, computed from noisy observations by the coder, play the role of latent variables. The approach is purely nonparametric, i.e., no prior finite-dimensional model is assumed. The answer to the question of how many latent variables should be used for an acceptable reconstruction accuracy of a family of functional data is inferred from learning based on the proposed approximation of the Akaike information Criterion. A distinguishing feature of this autoencoder is that the coder and encoder are designed as precomputed and stored matrices with the Bernstein polynomial entries. Thus, after selecting the number of latent variables, the autoencoder usage has a low computational complexity since it is linear with respect to observations. The proposed computational algorithms are tested on real data arising in mechanical engineering when control of damping vibrations is necessary. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Polynomials

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Towards All-optical Cavity-based Memory for Non-Gaussian Quantum State engineering

Towards All-optical Cavity-based Memory for Non-Gaussian Qua...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Hanamura, Fumiya Takase, Kan Hirota, Kazuki Nehra, Rajveer Lang, Florian Miki, Shigehito Terai, Hirotaka Yabuno, Masahiro China, Fumihiro Kahiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Asavanant, Warit Endo, Mamoru Yoshikawa, Jun-Ichi Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Department of Electrical and Computer Engineering University of Massachusetts-Amherst AmherstMA01003 United States Department of Physics University of Massachusetts-Amherst AmherstMA01003 United States Department of Information Technology and Electrical Engineering ETH Zurich Zurich8092 Switzerland Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi-ku Hyogo Kobe651-2492 Japan Graduate School of Engineering Kobe University 1-1 Rokkodai-cho Nada-ku Hyogo Kobe657-0013 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan

We present our experimental results on all-optical memory to store and manipulate quantum states, and propose resource-efficient time-domain-multiplexed state engineering using the memory. This paves a path to scalabl... 详细信息

ISBN: (纸本)9781957171395

关键词： Optical recording

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Non-Classical Quantum State Generation using Superconducting-Nanostrip Photon-Number Resolving Detector with Optical Sampling

Non-Classical Quantum State Generation using Superconducting...

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2024 Frontiers in Optics, FiO 2024

作者： Endo, Mamoru Takahashi, Kazuma Nomura, Takefumi Sonoyama, Tatsuki Miki, Shigehito Yabuno, Masahiro Terai, Hirotaka Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States

Superconducting-nanostrip photon detectors with optical sampling method now function as true photon-number resolving detectors in real-time without multiplexing. We applied this technique for quantum state generation ... 详细信息

关键词： Quantum computers

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An Adaptive-Gain Recurrent Neural Network for Solving the Joint-Angle Drift Issues of Redundant Manipulators

An Adaptive-Gain Recurrent Neural Network for Solving the Jo...

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2023 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2023

作者： Zheng, Boyu Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. School of Information Engineering Nanchang University Nanchang330031 China School of Automation Science and Engineering South China University of Technology Guangzhou510640 China College of Engineering Peking University Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

ISBN: (纸本)9798350309003

This paper proposes a new adaptive-gain recurrent neural network (AG-RNN) to effectively cope with the joint-angle drift issues in redundant manipulators. Specifically, a joint-angle drift-free with the feedback control of the velocity layer motion equation (JADF-FC) is proposed via an optimization criterion for synchronously optimizing linear terms and quadratic. Then, the JADF-FC is reasonably formulated into a standard quadratic programming (QP) issue. Different from the previous recurrent neural networks (RNNs), the AG-RNN proposed in this paper constructs an error-based differential equation with a new adaptive-gain. It should be noted that the proposed adaptive-gain does not gradually approach infinity as time increases, which is more in line with actual hardware implementation requirements than the existing time-variant-gain. The adaptive-gain can reduce the joint-angle drift errors of the redundant manipulator. Therefore, the proposed AG-RNN can solve the QP problem of the manipulator more effectively and quickly. To validate the performance of the proposed AG-RNN, it is compared with representative RNNs. The experimental results indicate that smaller joint-angle drift errors can be get by the proposed AG-RNN solving JADF-FC scheme than the other solutions when solving the joint-angle drift issues. © 2023 IEEE.

关键词： Quadratic programming

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Deep learning for brain age estimation: A systematic review

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information Fusion 2023年 96卷 130-143页

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Del Ser, Javier Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Silchar Assam 788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. © 2023 Elsevier B.V.

关键词： Deep neural networks

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Extending Kryszkiewicz’s Formula of Missing Values in Terms of Lipski’s Approach 1

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10th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2023

作者： Nakata, Michinori Saito, Norio Sakai, Hiroshi Fujiwara, Takeshi Faculty of Management and Information Science Josai International University 1 Gumyo Chiba Togane283-8555 Japan Department of Mathematics and Computer Aided Sciences Faculty of Engineering Kyushu Institute of Technology Tobata Kitakyushu804-8550 Japan Faculty of Informatics Tokyo University of Information Sciences 4-1 Onaridai Wakaba-ku Chiba265-8501 Japan

ISBN: (数字)9783031467813

ISBN: (纸本)9783031467806

When extracting any information from a data table with incomplete information, following Lipski we only know the lower and the upper bound of the information. Methods of rough sets that are applied to data tables containing incomplete information are examined from the viewpoint of Lipski’s approach based on possible world semantics. It is clarified that the formula that is first used by Kryszkiewicz, which most of the authors use, only gives the lower bound of the lower approximation and the upper bound of the upper approximation. When we focus on a value, this is because it is only considered that a missing value can be equal to that value. We extend Kryszkiewicz’s formula to consider that the missing value may not be equal to the value. As a result, the extended Kryszkiewicz’s formula gives the same approximations as those in terms of Lipski’s approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Semantics

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A robot process automation based mobile application for early prediction of chronic kidney disease using machine learning

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Discover Applied Sciences 2025年第6期7卷 1-34页

作者： Bijoy, Md. Hasan Imam Mia, Md. Jueal Rahman, Md. Mahbubur Arefin, Mohammad Shamsul Dhar, Pranab Kumar Shimamura, Tetsuya Department of Computer Science and Engineering Daffodil International University Dhaka Daffodil Smart City1216 Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram4349 Bangladesh Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo169-8555 Japan Department of Information and Computer Sciences Saitama University 255 Shimo-Okubo Sakura-ku Saitama338-8570 Japan

Chronic kidney disease (CKD) is characterized by persistent abnormalities in urinary biomarkers or reduced renal function, posing risks not only of progression to end-stage kidney disease but also of accelerated cardiovascular complications and mortality. The use of computer-aided automated diagnostics can assist nephrologists in early detection and accurate classification, which are essential for improving patient outcomes. This study utilized clinical features of CKD to develop and evaluate six base machine learning classifiers (logistic regression, K-nearest neighbors, AdaBoost, decision tree classifier, random forest, and multilayer perceptron) alongside two novel ensemble models (MKR Stacking and MKR Voting) for CKD prediction and classification. The proposed models were trained on five pre-processed CKD datasets using four robust feature selection techniques, including Lasso, Fisher score, information Gain, and Relief. The models’ performance was assessed using accuracy, precision, recall, F1-Score, error rate, AUC, and computational time. Among the tested algorithms, MKR Stacking achieved the highest accuracy of 99.50%, outperforming Random Forest (98.75%) and MKR Voting (98%). The XAI technique SHAP and model validation on another CKD dataset highlight the superior prediction capabilities of the proposed ensemble methods compared to traditional classification algorithms. The study further advocates for integrating high-performing models into the Internet of Medical Things and Robotic Process Automation frameworks, enabling real-time monitoring, predictive analytics, and efficient CKD diagnosis. Such integration has the potential to transform CKD management, facilitating early interventions and personalized treatment plans through advanced machine-learning applications. © The Author(s) 2025.

关键词： k-nearest neighbors

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Feasibility Analysis Study on Constructing a Grid Intrusion Detection System Using Semi-supervised Learning Models 2nd

Feasibility Analysis Study on Constructing a Grid Intrusion ...

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2nd International Conference on Security and information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Tsai, Chia-Wei Lin, Jason Yang, Chun-Wei Loo, Fu-Nie Department of Computer Science and Information Engineering National Taichung University of Science and Technology No.129 Sec.3 Sanmin Rd North District Taichung404 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung40227 Taiwan Master Program for Digital Health Innovation China Medical University No. 100 Sec. 1 Jingmao Rd. Beitun Dist. Taichung406040 Taiwan

ISBN: (纸本)9789819777853

In recent years, due to the proliferation of information and communication technology, as well as AI technology, industrial control systems, which were once in a closed network environment, have also integrated related technologies and gradually connected to external networks. Although this can enhance the efficiency of industrial production, it greatly increases the information security threats to industrial control systems. Among them, the information security issues of critical infrastructures are a key issue. If these critical infrastructures are attacked by cyber hackers, it could pose threats that jeopardize the safety of the general population. In light of this, this study focuses on the critical infrastructure of the power grid, proposing a semi-supervised intrusion detection system (IDS) technology by using the one-class classification. It analyzes data from the electricity distribution system to assess whether the system has been attacked. This research employs a power grid dataset to train the proposed model and execute the experiments. The experimental results demonstrate the feasibility of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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