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2024 IEEE International Conference on Big Data, BigData 2024

作者： Wolfgang, Seth Ruiter, Skyler Tunnell, Marc Triche, Timothy Carrier, Erin Debruine, Zachary University of Indiana Department of Intelligent Systems Engineering BloomingtonIN United States Purdue University Department of Computer Science West LafayetteIN United States Van Andel Institute Department of Epigenetics Grand RapidsMI United States Grand Valley State University College of Computing AllendaleMI United States

ISBN: (纸本)9798350362480

Genomics datasets, such as single-cell transcriptomics, are often very large and highly sparse, posing significant challenges for both storage and computation. As the scale of data generation accelerates, efficiently compressing these datasets becomes crucial. Current compression methods, like the popular Compressed Sparse Column (CSC) format, capitalize only on sparsity but overlook other properties like redundancy, which can offer additional opportunities for compression. Genomics data, especially single-cell assays, often exhibit high redundancy within columns, making traditional sparse formats inefficient for in-core computation. In this paper, we present two extensions to CSC: (1) Value-Compressed Sparse Column (VCSC) and (2) Index-and Value-Compressed Sparse Column (IVCSC). VCSC takes advantage of high redundancy within a column to further compress data up 1.9-fold over CSC on real data, without significant negative impact to performance characteristics. IVCSC extends VCSC by compressing index arrays through delta encoding and byte-packing, achieving up to a 4.4-fold decrease in memory usage over CSC on real data. Our benchmarks show that VCSC and IVCSC can be used in compressed form with little added computational cost. These formats represent a step forward in balancing the growing demands of data storage and processing in the era of large-scale genomics. © 2024 IEEE.

关键词： data redundancy matrix compression sparse data

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Hyperbolic Distance-Based Speech Separation

Hyperbolic Distance-Based Speech Separation

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Darius Petermann Minje Kim Department of Intelligent Systems Engineering Indiana University Bloomington IN USA Department of Computer Science University of Illinois at Urbana-Champaign IL USA

In this work, we explore the task of hierarchical distance-based speech separation defined on a hyperbolic manifold. Based on the recent advent of audio-related tasks performed in non-Euclidean spaces, we propose to make use of the Poincaré ball to effectively unveil the inherent hierarchical structure found in complex speaker mixtures. We design two sets of experiments in which the distance-based parent sound classes, namely "near" and "far", can contain up to two or three speakers (i.e., children) each. We show that our hyperbolic approach is suitable for unveiling hierarchical structure from the problem definition, resulting in improved child-level separation. We further show that a clear correlation emerges between the notion of hyperbolic certainty (i.e., the distance to the ball’s origin) and acoustic semantics such as speaker density, inter-source location, and microphone-to-speaker distance.

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Enhancing Photovoltaic Cell Parameters Extraction through Grey Wolf Optimizer

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IFAC-PapersOnLine 2024年第13期58卷 624-629页

作者： Charaf Chermite Moulay Rachid Douiri Control and Computer Science for Intelligent Systems and Green Energy Department of Applied Physics Faculty of Science and Technology Cadi Ayyad University 112 Bd Abdelkrim Al Khattabi Marrakech 40000 Morocco

Efficient and precise parameter extraction from solar Photovoltaic (PV) models is paramount for the comprehensive simulation, assessment, and management of PV systems. Despite the proliferation of analytical, numerical, and metaheuristic algorithms aimed at this task in recent years, the extraction of parameters remains a formidable obstacle. This study employs the Grey Wolf Optimizer (GWO) to extract the five key parameters of the RTC France solar cell. The GWO’s performance is systematically compared with metaheuristic algorithms such as Enhanced Chaotic JAYA (CJAYA) and Performance-Guided JAYA (PGJAYA). The study showcases the prowess of GWO in optimizing PV parameters, marking a significant stride forward in the realm of optimization techniques for PV cell modeling. Through meticulous analysis using MATLAB-SIMULINK, the research unveils the profound effectiveness of GWO in navigating the intricate landscape of parameter extraction within PV systems.

关键词： Photovoltaic cell Parameters extraction Metaheuristics algorithm Grey Wolf Optimizer

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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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Applied Mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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A Federated Learning Approach to Banking Loan Decisions

A Federated Learning Approach to Banking Loan Decisions

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Azzedin, Farag Ghaleb, Mustafa El-Alfy, Yasser Katib, Raed Hossain, M.D. King Fahd University of Petroleum and Minerals Information & Computer Science Department Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Saudi Arabia King Fahd University of Petroleum and Minerals Computer Engineering Department Dhahran Saudi Arabia

ISBN: (纸本)9798350335590

Machine Learning (ML) techniques for decision-making have become a topic of popular interest for many organizations. Such models, however, depend on data availability which is not always possible. Banks, for example, often have limited data about their customers. While this data could be supplemented with information from other banks, data sharing leads to privacy concerns due to regulations such as the General Data Protection Regulation (GDPR) and the California Customer Privacy Act (CCPA). An alternative method proposed to solve such a problem is Federated Learning (FL), where the data is not shared, but the models created by each entity are shared to keep privacy. FL has been applied across various sectors and industries, but not all. In this paper, we implement an FL algorithm in a new sector, namely, banking and loans. We used an architecture where multiple banks within the same country, each possessing different customer information, want to evaluate eligibility for loan provision. Our objective is to design an FL model where the banks share the data without exposing any customer hidden information. We implemented our FL model using Flower on the publicly available Loan dataset on Kaggle. Our model achieved an accuracy of 81%. © 2023 IEEE.

关键词： Risk assessment

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COVID-19 Lung CT Image Segmentation: A Comparison of Various U-Net Variants 2nd

COVID-19 Lung CT Image Segmentation: A Comparison of Various...

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2nd International Conference on intelligent Manufacturing and Robotics, ICIMR 2024

作者： Alebiosu, David Olayemi Folayan, Adeola Chen, Wei Tolulope, Abejide Ajibade, Samuel-Soma M. School of Engineering and Technology Department of Computing and Information Systems Sunway University Petaling Jaya Malaysia School of Medical and Health Science Monash University Petaling Jaya Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China College of Science and Engineering University of Derby Derby United Kingdom Department of Computing and Information Systems Sunway University Petaling Jaya Malaysia Department of Computer Engineering Istanbul Ticaret Universitesi Istanbul Turkey

ISBN: (纸本)9789819639489

Lung segmentation has become a bedrock in the effective diagnosis, and classification of coronavirus (COVID-19) from radiological images such as computed tomography (CT) and X-ray images. Since the coronavirus (COVID-19) discovery, several methods have been employed to segment the COVID-19-infected areas from lung CT images. One of the most popular segmentation methods is the U-Net model. U-Net is a convolutional neural network used for medical image segmentation. U-Net and its variants have become a more reliable architecture used for medical image segmentation. U-Net models have produced outstanding results in segmenting diseases such as COVID-19 from lung CT images. The exceptional results produced by the U-Net model have inspired various researchers to explore the potential of U-Net for various segmentation tasks. This study compares the performances of recently used state-of-the-art U-Net models on lung CT images for tuberculosis segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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Framework to generate hypergraphs with community structure

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Physical Review E 2024年第3期109卷 034309-034309页

作者： Nicolò Ruggeri Federico Battiston Caterina De Bacco Max Planck Institute for Intelligent Systems Cyber Valley 72076 Tübingen Germany Department of Computer Science ETH 8004 Zürich Switzerland Department of Network and Data Science Central European University 1100 Vienna Austria

In recent years hypergraphs have emerged as a powerful tool to study systems with multibody interactions which cannot be trivially reduced to pairs. While highly structured methods to generate synthetic data have proved fundamental for the standardized evaluation of algorithms and the statistical study of real-world networked data, these are scarcely available in the context of hypergraphs. Here we propose a flexible and efficient framework for the generation of hypergraphs with many nodes and large hyperedges, which allows specifying general community structures and tune different local statistics. We illustrate how to use our model to sample synthetic data with desired features (assortative or disassortative communities, mixed or hard community assignments, etc.), analyze community detection algorithms, and generate hypergraphs structurally similar to real-world data. Overcoming previous limitations on the generation of synthetic hypergraphs, our work constitutes a substantial advancement in the statistical modeling of higher-order systems.

关键词： Clustering Community structure Network formation & growth Network structure Patterns in complex systems

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Software Engineering and the Adoption of Internet of Things: A Systematic Literature Review

Software Engineering and the Adoption of Internet of Things:...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Mustafa Moosa Qasim Ali Raad Abdulkareem Intelligent Medical Systems Department College of Computer Science and Information Technology University of Basrah Iraq Computer Science and Information Technology Department College of Physical Education and Sport Science University of Basrah Basrah Iraq

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Recent interest in integrating Internet of Things (IoT) technology with software engineering has grown owing to its potential to transform several fields. The purpose of this research is to review the literature that are related to the integration of IoT with software engineering and explores the opportunities, challenges, and future directions. To fulfil this objective, a total of 22 articles related to the issues were reviewed using PRISMA. The findings showed that new software developments due to cloud computing and IoT need IoT-compatible architectures. Data security, authentication, access control, and trust management take precedence. The study also reveals managerial, architectural, security, interoperability, scalability, and professional training challenges in software engineering using IoT technologies. Despite these challenges, IoT is changing healthcare, smart cities, and industrial automation. The findings highlight the need for interdisciplinary collaboration and particular solutions to solve IoT-software engineering integration issues.

关键词： Access control Cloud computing Software architecture Smart cities Scalability Authentication Medical services

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