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Medical Imaging 2025: Digital and Computational Pathology

作者： Eskandari, Sania Eslamian, Ali Cheng, Qiang Department of Electrical and Computer Engineering University of Kentucky Lexington United States Department of Computer Science University of Kentucky Lexington United States Institute for Biomedical Informatics University of Kentucky Lexington United States

ISBN: (纸本)9781510686045

The classification of histopathological images is crucial for the early and precise detection of breast cancer. This study investigates the efficiency of deep learning models in distinguishing between Invasive Ductal Carcinoma (IDC) and non-IDC in histopathology slides. We conducted a thorough comparison examination of eight sophisticated models: ResNet-50, DenseNet-121, ResNeXt-50, Vision Transformer (ViT), GoogLeNet (Inception v3), EfficientNet, MobileNet, and SqueezeNet. This analysis was carried out using a large dataset of 277,524 image patches. Our research makes a substantial contribution to the field by offering a comprehensive assessment of the performance of each model. We particularly highlight the exceptional efficacy of attention-based mechanisms in the ViT model, which achieved a remarkable validation accuracy of 94%, surpassing conventional convolutional networks. This study highlights the promise of advanced machine learning approaches in clinical settings, offering improved precision as well as efficiency in breast cancer diagnosis. © 2025 SPIE.

关键词： Breast cancer Deep learning Tumor growth modeling Medical imaging Pathology Alternate lighting of surfaces computer engineering computer science Health informatics

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Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networks

arXiv

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arXiv 2024年

作者： Chen, Ziru Ni, Zhou Guan, Peiyuan Wang, Lu Cai, Lin X. Hashemi, Morteza Li, Zongzhi Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago United States Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Department of Informatics University of Oslo Oslo Norway Department of Civil Engineering Illinois Institute of Technology Chicago United States

Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy concerns. The Federated Vehicular Network (FVN) is a promising technology that tackles these concerns by transmitting model parameters instead of raw data, thereby protecting the privacy of drivers. Nevertheless, the performance of Federated Learning (FL) in a vehicular network depends on the joining ratio, which is restricted by the limited available wireless resources. To address these challenges, this paper proposes to apply Non-Orthogonal Multiple Access (NOMA) to improve the joining ratio in a FVN. Specifically, a vehicle selection and transmission power control algorithm is developed to exploit the power domain differences in the received signal to ensure the maximum number of vehicles capable of joining the FVN. Our simulation results demonstrate that the proposed NOMA-based strategy increases the joining ratio and significantly enhances the performance of the FVN. Copyright © 2024, The Authors. All rights reserved.

关键词： Data privacy

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Automatic Decomposition of a Sequential Algorithm for MapReduce Frameworks

Automatic Decomposition of a Sequential Algorithm for MapRed...

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2022 IEEE International Multi-Conference on engineering, computer and Information Sciences, SIBIRCON 2022

作者： Simonov, Victor S. Khairetdinov, Marat S. Novosibirsk State Technical University Dept. Computer Engineering Novosibirsk Russia Institute of Computational Mathematics and Mathematical Geophysics SB RAS Dept. Geophysical Informatics Novosibirsk Russia

ISBN: (纸本)9781665464802

Effective programming of parallel architectures has always been a difficult t ask. T o d ate, programming languages and technologies have been developed that simplify the programmer's work, but do not make parallelization automatic. MapReduce is a model of programming for the development of large-scale computations with intensive use of data. There are many frameworks where the implementation of this paradigm has been recently developed. There is a need to rewrite existing serial code to use the frameworks listed. The researcher must be familiar with the problems of parallelization, the API of the framework, and also have considerable experience. This prompted us to develop a new tool that automatically translates sequential programs into ready-made versions suitable for execution in the MapReduce paradigm. The code fragment from the serial version is converted in two stages. At the first stage, the synthesis of the program, the functional specification, was made. It was necessary to find information about the calculation structure for each block of code. The result was stored as a high-level intermediate language, reminiscent of the program format for MapReduce frameworks. Checking for semantic equivalence to the original has done using the proof of the theorem. At the second stage, executable code is created, which was the result of generation from a sequential program using the Hadoop or Spark instruction set. Creating a parallelizing compiler is one way to solve this problem. This will allow you to translate code written in a different paradigm (for example, imperative code) into a parallel version for the framework. Classical compilers, such as logical plan-to-physical compilers, use pattern matching rules. The compiler contains a set of rules that identify different patterns of code input (for example, list-sequential looping) and transform consistent code. © 2022 IEEE.

关键词： MapReduce

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KnowSOntoWSR: Web Service Recommendation System Using Semantically Driven QoS Ontology-Based Knowledge-Centred Paradigm 1

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7th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2022

作者： Dhanvardini, R. Deepak, Gerard Priyadarshini, J. Sheeba Santhanavijayan, A. Healthcare Informatics Division Optum UnitedHealth Groups Hyderabad India Department of Computer Science Engineering National Institute of Technology Tiruchirappalli India Department of Data Science Manipal Institute of Technology Bengaluru Bengaluru India Department of Data Science Manipal Academy of Higher Education Manipal India Bangalore India

ISBN: (数字)9789811997198

ISBN: (纸本)9789811997181

Web services have significantly expanded and become a key enabling technology for online data, application and resource sharing. Designing new methods for efficient and reliable web service recommendation has been of tremendous importance with the growing usage and prominence of web services. It would be ideal for a system to suggest online services that are in line with consumers’ preferences without requesting specific query information from them. Quality of Service (QoS) is vital for characterising non-functional aspects of Web services as they become more prevalent and widely used on the World Wide Web. The KnowSOntoWSR framework, which is built on a knowledge-driven and semantically inclined model that adheres to QoS ontology, is proposed in this research. AWS and WebSphere are employed as knowledge tags, and the powerful machine learning classifier XGBoost is applied. The features and recommendations are computed using the Twitter semantic similarity. The proposed framework outperforms the baseline models’ estimates with an accuracy of 95.94% and average F-measure of 95.93%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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The Calibration of Single Beam Distance Sensors based on Machine Learning Methods

The Calibration of Single Beam Distance Sensors based on Mac...

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IEEE International Conference on Big Data

作者： Piotr Biernacki Adam Ziebinski Jia-Hao Syu Jerry Chun-Wei Lin Institute of Theoretical and Applied Informatics Polish Academy of Sciences Institute of Informatics Silesian University of Technology Gliwice Poland Institute of Informatics Silesian University of Technology Gliwice Poland Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Smart cities require the use of many different types of sensors to make the communication, and distance sensors are one of the most commonly used elements in transportation systems and related infrastructures. The introduction of increasingly advanced autonomous systems in many areas of smart cities requires high measurement precision of the sensors used. High precision is essential for proper operation, long-term use, and safety in machine-to-machine or machine-to-human interactions. This paper presents a comparison of the accuracy of distance measurements for two commercially available single-beam LiDARs and two ultrasonic sensors. The aim of the research was to develop a calibration method in order to improve the accuracy of distance sensors. Based on the collected distance measurements, the sensors were calibrated using selected machine learning algorithms. The results of the experiments show the effectiveness of the proposed calibration methods, which yield an average mean absolute error (MAE) of 1.76 E-05 meters (m) and a root mean square error (RMSE) of 1.33 E-04 m for the tested sensors.

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The Relationship between Patient Perceived Pain and the Numerical Rating System: A Prospective Validation Using Electronic Health Record Data 16

The Relationship between Patient Perceived Pain and the Nume...

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16th International Congress on Nursing informatics, NI 2024

作者： Pruinelli, Lisiane Nguyen, Minh Stalter, Nicholas Johnson, Steven G. Simon, Gyorgy Speedie, Stuart Ma, Sisi College of Nursing University of Florida College of Medicine GainesvilleFL United States Institute on the Biology of Aging and Metabolism MinneapolisMN United States Department of Computer Science & Engineering University of Minnesota MinneapolisMN United States Institute for Health Informatics University of Minnesota MinneapolisMN United States School of Medicine University of Minnesota MinneapolisMN United States

ISBN: (纸本)9781643685274

We developed a method of using the Clinically Aligned Pain Assessment (CAPA) measures to reconstruct the Numeric Rating System (NRS). We used an observational retrospective cohort study design with prospective validation using de-identified adult patient data derived from a major health system. Data between 2011-2017 were used for development and 2018-2020 for validation. All included patients had at least one NRS and CAPA measurement at the same time. An ordinal regression model was built with CAPA components to predict NRS scores. We identified 6,414 and 3,543 simultaneous NRS-CAPA pairs in the development and validation dataset, respectively. All CAPA components were significantly related to NRS, with RMSE of 1.938 and Somers' D of 0.803 on the development dataset, and RMSE of 2.1 and Somers' D of 0.74 when prospectively validated. Our model was capable of accurately reconstructing NRS based on CAPA and was exact when the NRS was [0,7]. © 2024 The Authors.

关键词： Hospital data processing

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Weighted Sum of Segmented Correlation: an Efficient Method for Spectra Matching in Hyperspectral Images

Weighted Sum of Segmented Correlation: an Efficient Method f...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sampriti Soor Priyanka Kumari B. S. Daya Sagar Amba Shetty School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Department of Water Resources and Ocean Engineering NIT Karnataka India Systems Science and Informatics Unit Indian Statistical Institute Bangalore India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Matching a target spectrum with known spectra in a spectral library is a common method for material identification in hyperspectral imaging research. Hyperspectral spectra exhibit precise absorption features across different wavelength segments, and the unique shapes and positions of these absorptions create distinct spectral signatures for each material, aiding in their identification. Therefore, only the specific positions can be considered for material identification. This study introduces the Weighted Sum of Segmented Correlation method, which calculates correlation indices between various segments of a library and a test spectrum, and derives a matching index, favoring positive correlations and penalizing negative correlations using assigned weights. The effectiveness of this approach is evaluated for mineral identification in hyperspectral images from both Earth and Martian surfaces.

关键词： Image segmentation Correlation Absorption Shape Geoscience and remote sensing Libraries Iron

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Proceedings of the First International Workshop of Semantic Digital Humanities co-located with the Extended Semantic Web Conference 2024 1

Proceedings of the First International Workshop of Semantic ...

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1st International Workshop of Semantic Digital Humanities, SemDH 2024

作者： Bruns, Oleksandra Poltronieri, Andrea Stork, Lise Tietz, Tabea FIZ Karlsruhe Leibniz Institute for Information Infrastructure Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Kaiserstr. 89 Karlsruhe76133 Germany Department of Computer Science and Engineering University of Bologna Mura Anteo Zamboni 7 Bologna40126 Italy Informatics Institute University of Amsterdam Science Park 900 Amsterdam1098 XH Netherlands

Exploration, analysis, and preservation of the rich cultural and historical tapestry of the world are essential for our understanding of humanity’s past and shaping our future. In recent years, there has been increased interest in the creation and application of Ontologies, Knowledge Graphs, and other Semantic Web Technologies within cultural heritage (CH) and digital humanities (DH). However, to date, the distinct areas of expertise, methodologies and traditions across the fields have led to a noticeable gap between tech solutions and humanities’ needs. The aim of the International Workshop of Semantic Digital Humanities (SemDH) was to bridge this division and encourage closer collaboration and networking across diverse fields. © 2024 Copyright for this paper by its authors.

关键词： Ontology

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Label-Free Biomolecule Detection with InP/AlGaAs Charge Plasma Dielectric-Modulated Vertical TFET Using TaN as Metal Gate

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IEEE Access 2025年 13卷 89667-89684页

作者： Elshafie, Hashim Alqahtani, Abdulrahman Saad Mubarakali, Azath Adarsh Rag, S. Venkatesh, M. Parthasarathy, P. King Khalid University College of Computer Science Department of Computer Engineering Abha61421 Saudi Arabia University of Bisha College of Computing and Information Technology Bisha67714 Saudi Arabia King Khalid University College of Computer Science Department of Informatics and Computer Systems Abha61421 Saudi Arabia Manipal Institute of Technology Department of Data Science and Computer Applications Manipal Academy of Higher Education Manipal576104 India CMR Institute of Technology Department of Electronics and Communication Engineering Karnataka Bengaluru560037 India

This study introduces a label-free biosensing method for biomolecule detection utilizing an InP/AlGaAs charge plasma dielectric-modulated vertical tunnel field-effect transistor (InP/AlGaAs VTFET) featuring TaN as the metal gate. The device comprises a nanocavity beneath the gate metal adjacent to the tunnelling junction, where biomolecules engage with the dielectric material, resulting in fluctuations in the drain current. A twin metal gate architecture with laterally split dielectrics is employed to eliminate short-channel effects. The biosensor has exceptional sensitivity, attaining a peak drain current sensitivity of 2.5 × 10 cm2 for biomolecules such as albumin (k = 7). The gadget proficiently identifies biomolecules with varying dielectric constants and charge distributions, enabling adaptable label-free detection of diverse targets. The study examines the influence of dielectric constant on critical metrics such as Energy Band Diagrams, Drain Current, Drain Sensitivity, and subthreshold swing (SS). The overlap between the source and pocket regions, along with the introduction of an auxiliary gate, optimizes the electrical characteristics. Simulation results show that the proposed InP/AlGaAs VTFET achieves a maximum sensitivity of 3.5 × 103, outperforming configurations without overlap (2 × 103), highlights the potential of proposed InP/AlGaAs VTFET for scalable, high-sensitivity, label-free biomolecule detection. © 2013 IEEE.

关键词： Junction gate field effect transistors

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A Machine Learning-Based Privacy-Preserving Approach to Incorporate Distributed Generators in AC Optimal Power Flow

A Machine Learning-Based Privacy-Preserving Approach to Inco...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Burak Dindar Can Berk Saner Dogukan Yigit Polat Hüseyin Kemâl Çakmak Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Department of Electrical and Computer Engineering National University of Singapore Singapore School of Computing National University of Singapore Singapore

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time.

关键词： Data privacy Computational modeling Europe Machine learning computer architecture Generators Data models Standards Interoperability Load flow

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