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arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19

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Design and Implementation of Smart Street Lighting System Employing Esp-Mesh Network

SSRN

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SSRN 2023年

作者： Fuada, Syifaul Duttagupta, Subhasri Adiono, Trio Siregar, Lindawani Program Studi Sistem Telekomunikasi Universitas Pendidikan Indonesia West Java Province Bandung Indonesia Department of Computer Science and Engineering Amrita Viswa Vidyapeetham Keralam Amritapuri-Campus 690525 India Electrical Engineering Department School of Electrical Engineering and Informatics Institut Teknologi Bandung West Java Province Bandung Indonesia Teaching Factory of Manufacturing Electronics Politeknik Negeri Batam Kepulauan Riau Province Batam Indonesia

Street lighting is a public facility that gives highway users’ convenience during their nighttime trips, making it essential to be controlled and monitored using IoT technology. This study aimed to develop a smart lighting system connected through a Mesh network. The system monitors the street light and automatically controls it using the ESP8266 module, a microcontroller with Wi-Fi built-in. The options in selecting the lamp type include Halogen, Fluorescent, LED, or Incandescent. In general, LED is used because it has an advantage in energy-saving over other lamp types. Moreover, the system is equipped with automatic light intensity control through a dimming technique by Pulse Width Modulation (PWM), while BH1750 is used as a light sensor. Furthermore, it monitors the environmental temperature and humidity using DHT22. The communication between the lighting module is based on ESP-Mesh because each node is connected and forwarding data to the destination and the coverage area. ESP-Mesh allows the wireless network to have a scalable architecture in a broader area. It has features to self-organize and self-configure, implying the network could be built and maintained autonomously. Additionally, the system has a web server service with access to control each node and find out the mesh routing table. The results showed that each node could be configured as a mesh network using ESP8266, which sends dimming commands and reads the sensors. The dimming and intensity control was tested using real public street light called NL200D LED module and an off-the-self dimming control driver module, N-Channel MOSFET FQP30N06L. Furthermore, a Mesh connection was tested using five super bright 3mm LEDs representing real street light due to the limitation of the LED module in the laboratory. The street light has three control modes, including on/off-based, intensity control-based or dimming control manually through the website, and intensity control-based automatically using the two se

关键词： Mesh generation

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Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundational Models

arXiv

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arXiv 2024年

作者： Sankar, Baradwaj Simha Gilliland, Destiny Rincon, Jack Hermjakob, Henning Yan, Yu Adam, Irsyad Lemaster, Gwyneth Wang, Dean Watson, Karol Bui, Alex Wang, Wei Ping, Peipei Department of Physiology University of California Los AngelesCA United States NIH BRIDGE2AI Center UCLA NHLBI Integrated Cardiovascular Data Science Training Program at UCLA Los AngelesCA United States Cambridge United Kingdom Bioinformatics IDP University of California Los AngelesCA United States University of California Los AngelesCA United States Medical Informatics Home Area University of California Los AngelesCA United States Department of Computer Science University of California Los AngelesCA United States

Foundational Models (FMs) are gaining increasing attention in the biomedical AI ecosystem due to their ability to represent and contextualize multimodal biomedical data. These capabilities make FMs a valuable tool for a variety of tasks, including biomedical reasoning, hypothesis generation, and interpreting complex imaging data. In this review paper, we address the unique challenges associated with establishing an ethical and trustworthy biomedical AI ecosystem, with a particular focus on the development of FMs and their downstream applications. We explore strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings. Additionally, we emphasize the importance of key stewardship and co-design principles that not only ensure robust regulation but also guarantee that the interests of all stakeholders-especially those involved in or affected by these clinical and translational applications-are adequately represented. We aim to empower the biomedical AI community to harness these models responsibly and effectively. As we navigate this exciting frontier, our collective commitment to ethical stewardship, co-design, and responsible translation will be instrumental in ensuring that the evolution of FMs truly enhances patient care and medical decision-making, ultimately leading to a more equitable and trustworthy biomedical AI ecosystem. © 2024, CC BY.

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Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives

arXiv

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arXiv 2023年

作者： Jeon, Hyeongseon Xie, Juan Jeon, Yeseul Jung, Kyeong Joo Gupta, Arkobrato Chang, Won Chung, Dongjun Department of Biomedical Informatics The Ohio State University ColumbusOH United States Pelotonia Institute for Immuno-Oncology The James Comprehensive Cancer Center The Ohio State University ColumbusOH43210 United States The Interdisciplinary PhD program in Biostatistics The Ohio State University ColumbusOH United States Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Department of Applied Statistics Yonsei University Seoul Korea Republic of Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Division of Statistics and Data Science University of Cincinnati CincinnatiOH United States

Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis. © 2023, CC BY-NC-ND.

关键词： RNA

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Reporting of Hospital Facility on Smartphone

Reporting of Hospital Facility on Smartphone

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2019 International Conference of science and Information Technology in Smart Administration, ICSINTeSA 2019

作者： Rusdi, Jack Febrian Salam, Sazilah Abu, Nur Azman Sunaryo, Budi Agustina, Nova Gusdevi, Harya Hermansah Hadhiwibowo, Ari Heryati, Tuty Hardi, Richki Taufiq, Rohmat Jufri, Muhammad Taher Sudarsono, Ignatius Utomo, Eko Wahyu Kodong, Frans Richard Fannya, Puteri Informatics Engineering Sekolah Tinggi Teknologi Bandung Bandung Indonesia Information and Communication Technology Faculty Universiti Teknikal Malaysia Melaka Melaka Malaysia Department of Electrical Engineering Andalas University Padang Indonesia Rumah Sakit Umum Daerah Majalaya Kabupaten Bandung Majalaya Indonesia Informatics Engineering Computer Science Faculty Universitas Mulya Balikpapan Indonesia Department of Informatics Engineering Engineering Faculty University Muhammadiyah of Tangerang Tangerang15117 Indonesia Program Studi Sistem Informasi Fakultas Teknik Dan Sistem Informasi Universitas Yapis Papua Indonesia Civil Engineering Department Engineering Faculty Universitas Langlangbuana Bandung Bandung Indonesia Department of Informatics Engineering UPN Veteran Yogyakarta Yogyakarta Indonesia Department of Medical Record and Health Information Faculty of Health Universitas Esa Unggul Jakarta Indonesia

In the context of achieving Good Corporate Governance (GCG) in hospitals, among others, it is carried out with control and supervision, including in the case of hospital facility maintenance installations. Besides that, the hospital needs to improve its services. The asset reporting system is one of the high needs in this field, so it requires a solution that can deal with existing problems. Unfortunately, the current reporting system so far is more likely to be conventional, such as by telephone. This study proposes a new solution to the hospital facility maintenance reporting process, namely by utilising smartphone-based applications that directly connected to the server. So, the information distributed to the competent parties. This solution can speed up the reporting process and actions. Also, save the historical equipment that is available from each service room - the results of this study used as a hospital facility maintenance reporting system. © Published under licence by IOP Publishing Ltd.

关键词： Hospitals

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DCGAN-based Medical Image Augmentation to Improve ELM Classification Performance

DCGAN-based Medical Image Augmentation to Improve ELM Classi...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Rando Noor Akhmad Setiawan Adhistya Erna Permanasari Riries Rulaningtyas Andriyan Bayu Suksmono Imas Sukaesih Sitanggang Dept. of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia Dept. of Physics Universitas Airlangga Biomedical Engineering Program Surabaya Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department of Computer Science IPB University Bogor Indonesia

ISBN: (数字)9781665460309

ISBN: (纸本)9781665460316

Cervical cancer is one of the deadliest diseases in women. One of the cervical cancer screening methods is pap smear method. However, using a pap smear method to detect cervical cancer takes a long time for a pathologist to diagnose. Hence, a rapid development of medical computerization for early detection to get the results quickly is needed. This paper proposes synthetic data augmentation by using Deep Convolutional Generative Adversarial Network (DCGAN) to increase number of pap smear samples in dataset. Gray Level Co-occurrence Matrix (GLCM) is employed to extract features from dataset. Classification of 3 classes which are Adenocarcinoma, High-Grade Squamous Intraepithelial Lesion (HSIL), and Squamous Cell Carcinoma (SCC) is conducted using Extreme Learning Machine (ELM). The result shows that the addition of synthetic data improves the performance of ELM with the accuracy of 90%. This accuracy is better than the accuracy of ELM using only the original dataset which is 85%.

关键词： Satellites Extreme learning machines Medical services Feature extraction Generative adversarial networks Entropy Lesions

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Performance of multimodal prediction models for intracerebral hemorrhage outcomes using real-world data

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International Journal of Medical informatics 2025年 202卷 105989-105989页

作者： Matsumoto, Koutarou Suzuki, Masahiro Ishihara, Kazuaki Tokunaga, Koki Matsuda, Katsuhiko Chen, Jenhui Yamashiro, Shigeo Soejima, Hidehisa Nakashima, Naoki Kamouchi, Masahiro Department of Health Care Administration and Management of Medical Sciences Kyushu University Fukuoka Japan Graduate Degree Program of Applied Data Sciences Sophia University Tokyo Japan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Pharmacy Saiseikai Kumamoto Hospital Kumamoto Japan Department of Radiology Saiseikai Kumamoto Hospital Kumamoto Japan Division of Neurosurgery Saiseikai Kumamoto Hospital Kumamoto Japan Institute for Medical Information Research and Analysis Saiseikai Kumamoto Hospital Kumamoto Japan Medical Information Center Kyushu University Hospital Fukuoka Japan Department of Medical Informatics of Medical Sciences Kyushu University Fukuoka Japan Center for Cohort Studies of Medical Sciences Kyushu University Fukuoka Japan

Background: We aimed to develop and validate multimodal models integrating computed tomography (CT) images, text and tabular clinical data to predict poor functional outcomes and in-hospital mortality in patients with intracerebral hemorrhage (ICH). These models were designed to assist non-specialists in emergency settings with limited access to stroke specialists. Methods: A retrospective analysis of 527 patients with ICH admitted to a Japanese tertiary hospital between April 2019 and February 2022 was conducted. Deep learning techniques were used to extract features from three-dimensional CT images and unstructured data, which were then combined with tabular data to develop an L1-regularized logistic regression model to predict poor functional outcomes (modified Rankin scale score 3–6) and in-hospital mortality. The model's performance was evaluated by assessing discrimination metrics, calibration plots, and decision curve analysis (DCA) using temporal validation data. Results: The multimodal model utilizing both imaging and text data, such as medical interviews, exhibited the highest performance in predicting poor functional outcomes. In contrast, the model that combined imaging with tabular data, including physiological and laboratory results, demonstrated the best predictive performance for in-hospital mortality. These models exhibited high discriminative performance, with areas under the receiver operating curve (AUROCs) of 0.86 (95% CI: 0.79–0.92) and 0.91 (95% CI: 0.84–0.96) for poor functional outcomes and in-hospital mortality, respectively. Calibration was satisfactory for predicting poor functional outcomes, but requires refinement for mortality prediction. The models performed similar to or better than conventional risk scores, and DCA curves supported their clinical utility. Conclusion: Multimodal prediction models have the potential to aid non-specialists in making informed decisions regarding ICH cases in emergency departments as part of clinical dec

关键词： Logistic regression

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Protocol to explain support vector machine predictions via exact Shapley value computation

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STAR Protocols 2024年第2期5卷 103010页

作者： Mastropietro, Andrea Bajorath, Jürgen Deparment of Computer Control and Management Engineering “Antonio Ruberti” Sapienza University of Rome Via Ariosto 25 Rome 00185 Italy Department of Life Science Informatics and Data Science B-IT LIMES Program Unit Chemical Biology and Medicinal Chemistry Rheinische Friedrich-Wilhelms-Universität Friedrich-Hirzebruch-Allee 5/6 Bonn 53115 Germany Lamarr Institute for Machine Learning and Artificial Intelligence Friedrich-Hirzebruch-Allee 5/6 Bonn 53115 Germany

Shapley values from cooperative game theory are adapted for explaining machine learning predictions. For large feature sets used in machine learning, Shapley values are approximated. We present a protocol for two techniques for explaining support vector machine predictions with exact Shapley value computation. We detail the application of these algorithms and provide ready-to-use Python scripts and custom code. The final output of the protocol includes quantitative feature analysis and mapping of important features for visualization. For complete details on the use and execution of this protocol, please refer to Feldmann and Bajorath¹ and Mastropietro et al.² © 2024 The Author(s)

关键词： Bioinformatics Chemistry computer sciences

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Feature Selection in Cross-Project Software Defect Prediction 3

Feature Selection in Cross-Project Software Defect Predictio...

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3rd International Conference on science and Technology 2019, ICST 2019

作者： Saifudin, A. Trisetyarso, A. Suparta, W. Kang, C.H. Abbas, B.S. Heryadi, Y. Informatics Engineering Pamulang University Jalan Raya Puspitek 46 Banten15310 Indonesia Computer Science Program Bina Nusantara University Jalan Kebon Jeruk Raya 27 Jakarta11530 Indonesia Informatics Department Pembangunan Jaya University Banten Indonesia Department of Electronics and Communication Engineering Kwangwoon University Korea Republic of

Advances in technology have increased the use and complexity of software. The complexity of the software can increase the possibility of defects. Defective software can cause high losses. Fixing defective software requires a high cost because it can spend up 50% of the project schedule. Most software developers don't document their work properly so that making it difficult to analyse software development history data. Software metrics which use in cross-project software defects prediction have many features. Software metrics usually consist of various measurement techniques, so there are possibilities for their features to be similar. It is possible that these features are similar or irrelevant so that they can cause a decrease in the performance of classifiers. In this study, several feature selection techniques were proposed to select the relevant features. The classification algorithm used is Naive Bayes. Based on the analysis using ANOVA, the SBS and SBFS models can significantly improve the performance of the Naïve Bayes model. © Published under licence by IOP Publishing Ltd.

关键词： Defects

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vishakha D Bhandarkar Aruna Sri Rongali Mohamed Dawood Shamout Prof. Sachin Pund Gigih Forda Nama Shrishailappa Patil Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Computer engineering Vishwakarma Institute of Technology Pune

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..

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