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Medical Imaging 2025: Digital and Computational Pathology

作者： Rudravaram, Gaurav Bao, Shunxing Krishnan, Aravind R. Remedios, Lucas W. Kim, Michael E. Saunders, Adam M. Liu, Qi Lau, Ken S. Roland, Joseph T. Washington, Mary K. Coburn, Lori A. Wilson, Keith T. Huo, Yuankai Landman, Bennett A. Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Division of Gastroenterology Hepatology and Nutrition Department of Medicine Vanderbilt University Medical Center NashvilleTN United States Vanderbilt Center for Mucosal Inflammation and Cancer Vanderbilt University Medical Center NashvilleTN United States Program in Cancer Biology Vanderbilt University School of Medicine NashvilleTN United States Veterans Affairs Tennessee Valley Healthcare System NashvilleTN United States Department of Pathology Microbiology and Immunology Vanderbilt University Medical Center NashvilleTN United States Center for Quantitative Sciences Vanderbilt University Medical Center NashvilleTN United States Epithelial Biology Center Vanderbilt University Medical Center NashvilleTN United States Department of Cell and Developmental Biology Vanderbilt University School of Medicine NashvilleTN United States Vanderbilt University Department of Biomedical Engineering Nashville United States Department of Biostatistics Vanderbilt University Medical Center Nashville United States

ISBN: (纸本)9781510686045

Crohn's disease (CD) is a chronic inflammatory condition of the gastrointestinal tract characterized by alternating periods of activity and remission. This study investigates whether the cellular environments of quiescent (inactive disease) and normal tissues show similarities or differences, exploring potential residual effects from disease and tissue recovery patterns. While active CD can typically be characterized by high neutrophil density, whereas quiescent tissues present less pronounced inflammatory markers. This study leverages multiplexed immunofluorescence (MxIF) data to examine the distributions and patterns of various nuclei subtypes in quiescent versus normal tissues, aiming to determine how similar or different these tissues are in the absence of active inflammation. In our study, we analyze four tissue samples labeled as normal and five as quiescent from CD patients, as determined by a pathologist. We use rigid and deformable registration of MxIF images to analyze these tissues in a unified space and classify the nuclei into 13 subtypes. Using a KD tree, we identify the 10 nearest neighbors for each nucleus, building a combined distance-weighted neighborhood matrix representing both normal and quiescent tissues. We projected these neighborhoods into 2 dimensions using t-distributed stochastic nearest neighbor(t-SNE), clustered them, and calculated the enrichment for each subtype between normal and quiescent states within each of 20 and 40 clusters. In the 20-cluster analysis, we find monocytes enrich in one cluster and leukocytes enrich in two clusters for normal and quiescent tissues respectively. For the 40-cluster analysis, we observe monocytes, macrophages, and leukocytes enrich in one cluster each in normal tissues, and in the quiescent condition, one cluster enriched with enteroendocrine cells, and two clusters enriched with leukocytes. The results were statistically significant as determined by a chi-squared test (p © 2025 SPIE.

关键词： Tissues White blood cells Diseases and disorders Biological samples Matrices Statistical analysis Image segmentation Image registration Critical dimension metrology Inflammation

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Optimization of Node Reduction Using BRKGA for GNN-Based Traffic Speed Forecasting 13

Optimization of Node Reduction Using BRKGA for GNN-Based Tra...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Inokuchi, Yuto González, Pedro Henrique Mendonça, Israel Aritsugi, Masayoshi Graduate School of Science and Technology Kumamoto University Kumamoto Japan Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350308365

While Spatio-Temporal Graph Convolutional Networks (STGCNs) are an effective method for traffic speed fore-casting, their training and inference tend to be time-consuming. In this paper, we aim to refine these networks by strategically reducing their number of nodes, thereby boosting computational efficiency. The nodes in these graphs represent data observed for road segments, and by analyzing the interconnections and layout of the graph, we can identify nodes with minimal contribution to overall performance. Removing these nodes can potentially decrease computation time while maintaining the prediction accuracy. We employ the Biased Random-Key Genetic Algorithm (BRKGA) to identify a good set of nodes for removal, based on a predefined percentage reduction of the original graph size (e.g., retaining 95 % of the original graph). We evaluate different graph size configurations, ranging from 95 % to 70 % node retention, to determine the least impactful node set performance. Our experiments on three real-world datasets reveal that reducing nodes can decrease computation time by up to 29%, and as a byproduct of removing noise, even improve the prediction accuracy. © 2024 IEEE.

关键词： Genetic algorithms

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Efficient Hyperparameter Optimization Using Deep Q-Network and BRKGA 13

Efficient Hyperparameter Optimization Using Deep Q-Network a...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Kobayashi, Kosei Aritsugi, Masayoshi González, Pedro Henrique Mendonça, Israel Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9798350308365

Hyperparameter optimization (HPO) is paragon to maximize performance when designing machine learning models. Among different HPO methods, Genetic Algorithm (GA) based optimization is considered effective because it allows a wide and diverse range of solutions to be explored. However, GA's exploratory nature makes this type of algorithm to evaluate many solutions that do not improve the overall performance. This is specially costly when the objective function to be evaluated is time-consuming, like in the HPO field. In this paper, we propose an efficient hybrid algorithm that is able to reduce computational cost by combining deep reinforcement learning with the Biased Random Key Genetic Algorithm (BRKGA), a variant of genetic algorithms. Our reinforcement learning agent has a decision-making role during the population's fitness calculation, in which it filters out chromosomes that would not improve the overall fitness of the population. The agent uses small amounts of pre-trained data to identify trends in potentially good solutions, and carry out its decision process. We conduct experiments on eight different datasets to assess the effectiveness of the proposed method, and the results show that the proposed method can significantly reduce the computation time of hyperparameter search using BRKGA (up to 44% reduction in computational time) without compromising the quality of the solution (no statistically difference in results). © 2024 IEEE.

关键词： Genetic algorithms

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AKIP Process Automation Platform: A Framework for the Development of Process-Aware Web Applications 18

AKIP Process Automation Platform: A Framework for the Develo...

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18th International Conference on Web Information systems and Technologies, WEBIST 2022

作者： Neto, Ulisses Telemaco Oliveira, Toacy Pillat, Raquel Alencar, Paulo Cowan, Don Melo, Glaucia David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada System Engineering and Computing Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9789897586132

An increasing number of platforms for Business Process Automation (BPA) have been developed in recent years, including open-source and proprietary solutions. However, there are still some unsolved problems and limitations related to the adoption of these solutions, which include: vendor lock-in, limited UI/UX, limited integration, outdated technology stack, and lack of support for non-process features. The framework presented in this paper addresses these problems and limitations by providing an open-source platform to facilitate the development of process-aware information systems (PAISs) based on code generation techniques. The platform is capable of generating a functional process-aware Web application from a business process model defined in BPMN. To the best of our knowledge, there is no other software tool that generates fully functional process-aware Web applications. The presented framework has been evaluated in the academy and industry and used to develop dozens of non-profit and commercial process-aware applications. Copyright © 2022 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Open systems

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Scaffolding Process-Aware Information systems with the AKIP Platform 1

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18th International Conference on Web Information systems and Technologies, WEBIST 2022

作者： Telemaco Neto, Ulisses Oliveira, Toacy Pillat, Raquel Alencar, Paulo Cowan, Don Melo, Glaucia David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada System Engineering and Computing Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9783031430886

ISBN: (纸本)9783031430879

Business Process Automation (BPA) refers to the automation of business processes through technology, with the goal of improving efficiency, reducing errors, and increasing productivity by eliminating manual and repetitive tasks. Over the past few years, the number of BPA platforms has increased, including both open-source and proprietary solutions. However, some challenges and limitations still exist related to the adoption of these solutions, such as vendor lock-in, limited UI/UX, limited integration, outdated technology stack, and lack of support for non-process features. To address these issues, this paper presents the AKIP platform, an open-source framework for developing process-aware information systems (PAISs) using code generation techniques. AKIP generates functional process-aware web applications from BPMN business process models, making it the only known software tool capable of generating fully functional process-aware web applications. To evaluate the effectiveness of the AKIP platform, a case study was conducted in the industry and six business processes automated into a process-oriented web application were analyzed in this paper. The study showed that the AKIP platform was able to generate a functional web application while supporting the automation of business processes with different model sizes and complexities. Furthermore, this study showed that even a team of professionals with little experience was able to produce such results by using the AKIP platform. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Automation

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A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

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computers, Materials & Continua 2023年第11期77卷 1557-1573页

作者： Tanvir Fatima Naik Bukht Naif Al Mudawi Saud S.Alotaibi Abdulwahab Alazeb Mohammed Alonazi Aisha Ahmed AlArfaj Ahmad Jalal Jaekwang Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Information Systems Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Convergence Program for Social Innovation Sungkyunkwan UniversitySuwon03063Korea

Human-human interaction recognition is crucial in computer vision fields like surveillance,human-computer interaction,and social *** enhances systems’ability to interpret and respond to human behavior *** research focuses on recognizing human interaction behaviors using a static image,which is challenging due to the complexity of diverse *** overall purpose of this study is to develop a robust and accurate system for human interaction *** research presents a novel image-based human interaction recognition method using a Hidden Markov Model(HMM).The technique employs hue,saturation,and intensity(HSI)color transformation to enhance colors in video frames,making them more vibrant and visually appealing,especially in low-contrast or washed-out *** filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical *** extraction uses the features from Accelerated Segment Test(FAST),Oriented FAST,and Rotated BRIEF(ORB)*** application of Quadratic Discriminant Analysis(QDA)for feature fusion and discrimination enables high-dimensional data to be effectively analyzed,thus further enhancing the classification *** ensures that the final features loaded into the HMM classifier accurately represent the relevant human *** impressive accuracy rates of 93%and 94.6%achieved in the BIT-Interaction and UT-Interaction datasets respectively,highlight the success and reliability of the proposed *** proposed approach addresses challenges in various domains by focusing on frame improvement,silhouette and feature extraction,feature fusion,and HMM *** enhances data quality,accuracy,adaptability,reliability,and reduction of errors.

关键词： Human interaction recognition HMM classification quadratic discriminant analysis dimensionality reduction

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Efficient Node Reduction Heuristic for GNN-Based Traffic Speed Forecasting 11

Efficient Node Reduction Heuristic for GNN-Based Traffic Spe...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Inokuchi, Yuto Gonzalez, Pedro Henrique Aritsugi, Masayoshi Mendonca, Israel Kumamoto University Graduate School of Science and Technology Kumamoto Japan Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350367300

In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, classification, and clustering, due to their ability to learn node and edge attributes and relationships, and they have been utilized for intelligent transportation systems recently by converting sensor networks into graph structures. Deep spatio-temporal neural networks, including Spatio-Temporal Graph Convolutional Networks (STGCNs), capture spatial and temporal dependencies, making them suitable for traffic speed forecasting, traffic demand prediction, and travel time estimation. Despite their success, GNNs face challenges in industrial applications due to significant memory usage and time consumption. In this paper, we propose a new approach to node reduction that outperforms existing methods in computational efficiency. Our experiments on two real-world traffic datasets demonstrate that using the heuristic and edge information to reduce nodes can cut computation time of optimization up to 95% and, by eliminating noise, can even enhance prediction accuracy. © 2024 IEEE.

关键词： Graph neural networks

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Disclosure Privacy Information Social Media on TikTok 9

Disclosure Privacy Information Social Media on TikTok

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Syamsuar, Dedy Fahrezi, Fazri Favian, Ahnaf Irwansyah, Edy Irawan, Achmad Fakhri School of Information System Bina Nusantara University Faculty of Engineering Professional Engineer Program Dept. Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

With the rapid development of technology, the use of social media by the public, especially among young people, is increasing. One of the social media platforms currently used by young people is the TikTok application. It is a video-based TikTok feature accompanied by music, writing, and pictures that are considered attractive, so teenagers like it to show their existence and self-disclosure. Therefore, this study aims to examine the intention of users to disclose their privacy. As the basis of the theory, this study deployed the privacy calculus theory, where the perceived benefits and perceived risks play crucial roles in the intention to disclose their privacy. The study successfully collected 369 responses. The empirical findings indicated that only the relationship between Perceived Personalization (PP) and Perceived Risk (PR) was not supported. However, all the remaining hypotheses were supported. These findings emphasize how several complicated variables influence social media users' intentions to disclose their privacy. When people feel in control of their privacy settings and see significant benefits, they are more likely to divulge personal information;nevertheless, perceived risks can greatly discourage users. © 2024 IEEE.

关键词： Differential privacy

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Feature Weighting Optimization: Genetic Algorithms and Random Forest for Classification of Pregnant Potential Risk

Feature Weighting Optimization: Genetic Algorithms and Rando...

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2022 International Conference on Information Technology Research and Innovation, ICITRI 2022

作者： Ramdhani, Yudi Maulidia, Dea Setiadi, Ahmad Alamsyah, Doni Purnama Universitas Adhirajasa Reswara Sanjaya Informatics Engineering Program Bandung Indonesia Universitas Adhirajasa Reswara Sanjaya Computer Science Program Bandung Indonesia Universitas Bina Sarana Informatika Information System Program Jakarta Indonesia Bina Nusantara University Entrepreneurship Department Jakarta Indonesia

ISBN: (数字)9781665461849

ISBN: (纸本)9781665461849

Many pregnant women still do not want to check their pregnancy to recognize the signs of high-risk pregnant women. High-risk pregnancies refer to abundant women with several risky conditions requiring special care during and after delivery. This study aims to predict pregnancy risk levels with early detection efforts. The method used in this research is the Random Forest method for classification and the Genetic Algorithm for optimization. The results obtained from the Maternal Health Risk dataset analysis using the Random Forest method get an accuracy value of 73.37%. After optimizing the features using a genetic algorithm, the accuracy value in the classification of the health risk level of pregnant women has increased significantly to 90.20%. The results of the t-Test in this study showed a value of less than 0.05, and the t-Test value obtained is 0.000218269. That is, the genetic algorithm has excellent performance to improve the performance of the classification algorithm. Based on the results of the study to get a good accuracy value, it can be used by medical personnel to detect the level of health risk in pregnant women. It can be a reference material for further research to implement this research into a program. © 2022 IEEE.

关键词： Genetic algorithms

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Exploring Low Rate Dos Attack Detection Methods: A Bibliometric Analysis and Roadmap for Further Research

Exploring Low Rate Dos Attack Detection Methods: A Bibliomet...

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2023 IEEE International Conference on Data and Software engineering, ICoDSE 2023

作者： Widodo, Anteng Rochim, Adian Fatchur Warsito, Budi School of Postgraduated Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Muria Kudus University Faculty of Engineering Department of Information System Kudus Indonesia Diponegoro University Faculty of Engineering Department of Computer Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Statistics Semarang Indonesia

ISBN: (纸本)9798350381382

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future. copy;2023 IEEE. © 2023 IEEE.

关键词： Denial-of-service attack

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