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IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)

作者： Yaya Heryadi Edy Irwansyah Eka Miranda Haryono Soeparno Herlawati Kiyota Hashimoto Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Faculty of Technology and Environment Prince of Songkla University Phuket Thailand

Food sustainability is still one of the main priorities for many countries as it contributes to the economy and stability of the nation. For government in many countries whose peoples consumes rice as its staple food, food self-sufficiency initiatives highly depend on accurate prediction of paddy field map. Mapping paddy field task is a challenging problem which cannot be handled manually especially when the paddy fields are spread out in very wide geographical areas such as those in Indonesia. Fortunately, wide availability of satellite imagery and the advent of deep learning technology in the past ten years have made it possible to improve efficiency of most parts of those manual works involving image semantic segmentation tasks. However, satellite image-based semantic segmentation is a challenging task. High object complexity, cloud partial occlusion, larger image size than a computer memory can stored can hinder accuracy of the image segmentation results. This paper presents a method for paddy field map generating using semantic image segmentation approach in which Pyramid Scene Parsing Net model is used for segmenting satellite imagery. The generated paddy map can be used as a basis for decision-making, especially in the agricultural sector. Analysis of local land use/land cover dynamics. The results of his experiments using SPOT 6 satellite imagery from the Pahung region of Central Kalimantan achieved average training accuracy, best training accuracy and test accuracy of 0.85, 0.86 and 0.89 respectively. These results indicated that the semantic segmentation model is suitable for addressing the same task in different crops.

关键词： Training Productivity Deep learning Satellites Semantic segmentation Crops Predictive models

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FedMonitor: An Implementation Architecture for Monitoring of Resources and Events'Notifications on Federated Cloud Computing Infrastructures

FedMonitor: An Implementation Architecture for Monitoring of...

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International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)

作者： Emmanuel Adetiba Uche Nnaji John S. Wejin Festus Ojesanmi Matthew B. Akanle Priscilla O. Ajayi Faith O. Sweetwiliams Surendra C. Thakur Ezekiel F. Adebiyi Department of Electrical and Information Engineering and Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Computer and Information Science & CApIC-ACE Covenant University Ota Nigeria Department of Electrical and Information Engineering & CApIC-ACE Covenant University Jalingo Nigeria Department of Computer Science Tareba State University Jalingo Nigeria Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria Department of Electrical and Information Engineering Covenant University & CApIC-ACE Covenant University Ota Nigeria Center for Systems and Information Services & CApIC-ACE Covenant University Ota Nigeria Department of Information Technology and KZN e-Skills Co-Laboratory Durban University of Technology Durban South Africa Covenant University Bioinformatics Research (CUBRe) Covenant University Ota Ogun State Nigeria African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala Uganda

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

In the evolving landscape of cloud computing, federated cloud infrastructures present unique challenges and opportunities for resource and application monitoring. Monitoring the diverse array of cloud resources and applications within a federated cloud environment is crucial for delivering consistent Quality of Service (QoS) and continuous oversight across various cloud applications and infrastructure. Despite the various successes documented in the literature on multi-cloud monitoring, existing frameworks have not fully explored or provided enough insights into centralized monitoring of resources, applications, and network devices in a federated cloud environment. This paper introduces FedMonitor, an implementation architecture designed to enhance the monitoring and notification capabilities in federated cloud environments by building on the theoretical ideals in our earlier work (i.e. FEDARGOS-V1). FedMonitor leverages Prometheus and Grafana to crawl, manage and visualize metrics from diverse software services, hardware resources, and network devices within a federated cloud environment. The results demonstrate hat FedMonitor improves the effectiveness of resource and application monitoring in federated cloud environments.

关键词： Measurement Cloud computing Visualization Computer architecture Quality of service Production Software Real-time systems Stakeholders Monitoring

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Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries

Hybrid genetic-discretized algorithm to handle data uncertai...

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作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Abdar, Moloud Beykikhoshk, Adham Khosravi, Abbas Nahavandi, Saeid Plawiak, Pawel Tan, Ru San Acharya, U Rajendra Deakin University GeelongVIC Australia Department of Engineering Fasa Branch Islamic Azad University Fars Fasa Iran Centre for Pattern Recognition and Data Analytics Deakin University GeelongVIC Australia Department of Information and Communications Technology Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 st. F-3 31-155 Krakow Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland National Heart Centre Singapore Singapore Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which arteries are stenotic, most of the researchers focus only on CAD detection and few studies have investigated stenosis of the right coronary artery (RCA), left circumflex (LCX) artery and left anterior descending (LAD) artery separately. Meanwhile, most of the datasets in this field are noisy (data uncertainty). However, to the best of our knowledge, there is no study conducted to address this important problem. This study uses the extension of the Z-Alizadeh Sani dataset, containing 303 records with 54 features. A new feature selection algorithm is proposed in this work. Meanwhile, by discretization of data, we also handle the uncertainty in CAD prediction. To the best of our knowledge, this is the first study attempted to handle uncertainty in CAD prediction. Finally, the genetic algorithm (GA) is used to determine the hyper-parameters of the support vector machine (SVM) kernels. We have achieved high accuracy for the stenosis diagnosis of each main coronary artery. The results of this study can aid the clinicians to validate their manual stenosis diagnosis of RCA, LCX and LAD coronary arteries. © 2020 John Wiley & Sons, Ltd.

关键词： Feature Selection

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HECLIP: Histology-Enhanced Contrastive Learning for Imputation of Transcriptomics Profiles

arXiv

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arXiv 2025年

作者： Wang, Qing Chen, Wen-Jie Li, Bo Su, Jing Wang, Guangyu Song, Qianqian Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida FL32611 United States School of Biological and Behavioural Sciences Queen Mary University of London LondonE1 4NS United Kingdom Department of Computer and Information Science University of Macau Macau SAR Taipa999078 China Department of Biostatistics and Health Data Science Indiana University School of Medicine IN46202 United States Center for Bioinformatics and Computational Biology Houston Methodist Research Institute TX77030 United States Department of Cardiothoracic Surgery Weill Cornell Medicine Cornell University NY United States

Histopathology, particularly hematoxylin and eosin (H&E) staining, plays a critical role in diagnosing and characterizing pathological conditions by highlighting tissue morphology. However, H&E-stained images inherently lack molecular information, requiring costly and resource-intensive methods like spatial transcriptomics to map gene expression with spatial resolution. To address these challenges, we introduce HECLIP (Histology-Enhanced Contrastive Learning for Imputation of Profiles), an innovative deep learning framework that bridges the gap between histological imaging and molecular profiling. HECLIP is specifically designed to infer gene expression profiles directly from H&E-stained images, eliminating the need for expensive spatial transcriptomics assays. HECLIP leverages an advanced image-centric contrastive loss function to optimize image representation learning, ensuring that critical morphological patterns in histology images are effectively captured and translated into accurate gene expression profiles. This design enhances the predictive power of the image modality while minimizing reliance on gene expression data. Through extensive benchmarking on publicly available datasets, HECLIP demonstrates superior performance compared to existing approaches, delivering robust and biologically meaningful predictions. Detailed ablation studies further underscore its effectiveness in extracting molecular insights from histology images. Additionally, HECLIP’s scalable and cost-efficient approach positions it as a transformative tool for both research and clinical applications, driving advancements in precision medicine. The source code for HECLIP is openly available at https://***/QSong-github/HECLIP. © 2025, CC BY-NC-ND.

关键词： Molecular imaging

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data Extraction for Associative Classification using Mined Rules in Pediatric Intensive Care data

Data Extraction for Associative Classification using Mined R...

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2023 Datenbanksysteme fur Business, Technologie und Web, BTW 2023 - 2023 database Systems for Business, Technology and Web, BTW 2023

作者： Das, Pronaya Prosun Mast, Marcel Wiese, Lena Jack, Thomas Wulff, Antje Bode, Louisa Frank, Torsten Marschollek, Michael Schamer, Sven Rathert, Henning Haller, Andreas Beerbaum, Philipp Rübsamen, Nicole Böhnke, Julia Karch, André Das, Pronaya Prosun Groszweski-Anders, Christian Fraunhofer Institute for Toxicology and Experimental Medicine Hannover Germany Peter L. Reichertz Institute for Medical Informatics TU Braunschweig Hannover Medical School Hannover Germany Institute of Computer Science Goethe University Frankfurt Frankfurt a. M. Germany Department of Pediatric Cardiology and Intensive Care Medicine Hannover Medical School Hannover Germany Big Data in Medicine Department of Health Services Research School of Medicine and Health Sciences Carl von Ossietzky University Oldenburg Oldenburg Germany Institute of Epidemiology and Social Medicine University of Muenster Muenster Germany Research Group Bioinformatics Fraunhofer Institute for Toxicology and Experimental Medicine Hannover Germany Medisite GmbH Hannover Germany

ISBN: (纸本)9783885797258

Based on the characteristics of health and medical informatics, data mining techniques that were designed to tackle healthcare problems are faced with new challenges. One such challenge is to prepare medical data for pattern mining or machine learning. In this paper, we present a feature engineering technique for the Associative Classification of the Systemic Inflammatory Response Syndrome (SIRS) in severely ill children by mining Associative Rules. SIRS is characterized as the body's excessive defense response due to malevolent stressors such as trauma, acute inflammation, infection, malignancy, and surgery. It can have an impact on the clinical outcome and elevate vulnerability for organ dysfunctions. We aim to extract the features from given datasets using the described extraction process. After the transformation, those features are used to mine rules using Association Rule Mining. Those rules are used to perform Associative Classification and evaluated with the result generated by SIRS criteria defined by the experienced clinicians. The mined rules provide better control over sensitivity and specificity than the SIRS criteria used in everyday medical practice.. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： data mining

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Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes

arXiv

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arXiv 2023年

作者： Liu, Xueqing Qian, Tianchen Bell, Lauren Chakraborty, Bibhas Centre for Quantitative Medicine Duke-NUS Medical School Singapore Department of Statistics University of California Irvine IrvineCA United States Medical Research Council Biostatistics Unit University of Cambridge Cambridge United Kingdom Department of Medical Statistics The London School of Hygiene and Tropical Medicine London United Kingdom Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States

In mobile health, tailoring interventions for real-time delivery is of paramount importance. Microrandomized trials have emerged as the "gold-standard" methodology for developing such interventions. Analyzing data from these trials provides insights into the efficacy of interventions and the potential moderation by specific covariates. The "causal excursion effect", a novel class of causal estimand, addresses these inquiries. Yet, existing research mainly focuses on continuous or binary data, leaving count data largely unexplored. The current work is motivated by the Drink Less micro-randomized trial from the UK, which focuses on a zero-inflated proximal outcome, i.e., the number of screen views in the subsequent hour following the intervention decision point. To be specific, we revisit the concept of causal excursion effect, specifically for zero-inflated count outcomes, and introduce novel estimation approaches that incorporate nonparametric techniques. Bidirectional asymptotics are established for the proposed estimators. Simulation studies are conducted to evaluate the performance of the proposed methods. As an illustration, we also implement these methods to the Drink Less trial data. © 2023, CC BY.

关键词： Binary alloys

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Exploring causal effects of hormone- and radio-treatments in an observational study of breast cancer using copula-based semi-competing risks models

arXiv

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arXiv 2024年

作者： Yu, Tonghui Peng, Mengjiao Cui, Yifan Chen, Elynn Chen, Chixiang School of Physical and Mathematical Sciences Nanyang Technological University Singapore School of Statistics East China Normal University China Center for Data Science Zhejiang University China Stern School of Business New York University New YorkNY United States Division of Biostatistics and Bioinformatics Department of Epidemiology and Public Health University of Maryland School of Medicine BaltimoreMD United States Department of Neurosurgery University of Maryland School of Medicine BaltimoreMD United States

Breast cancer patients may experience relapse or death after surgery during the follow-up period, leading to dependent censoring of relapse. This phenomenon, known as semi-competing risk, imposes challenges in analyzing treatment effects on breast cancer and necessitates advanced statistical tools for unbiased analysis. Despite progress in estimation and inference within semi-competing risks regression, its application to causal inference is still in its early stages. This article aims to propose a frequentist and semi-parametric framework based on copula models that can facilitate valid causal inference, net quantity estimation and interpretation, and sensitivity analysis for unmeasured factors under right-censored semi-competing risks data. We also propose novel procedures to enhance parameter estimation and its applicability in real practice. After that, we apply the proposed framework to a breast cancer study and detect the time-varying causal effects of hormone- and radio-treatments on patients' relapse-free survival and overall survival. Moreover, extensive numerical evaluations demonstrate the method's feasibility, highlighting minimal estimation bias and reliable statistical inference. © 2024, CC BY-NC-ND.

关键词： Sensitivity analysis

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

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Procedia Computer science 2023年 218卷 2786-2794页

作者： Seifedine Kadry Enrique Herrera-Viedma Rubén González Crespo Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Granada Andalusian Spain Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai 600119 Tamil Nadu India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases; (i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods.

关键词： Lung nodule CT scan CNN segmentation VGG-SegNet Accuracy Detection

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'EMDB-the Electron Microscopy data Bank' (vol 1, gkad1019, 2023)

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NUCLEIC ACIDS RESEARCH 2023年第D1期52卷 D1701-D1701页

作者： Turner, Jack Abbott, Sanja Fonseca, Neli Pye, Ryan Carrijo, Lucas Duraisamy, Amudha Kumari Salih, Osman Wang, Zhe Kleywegt, Gerard J. Morris, Kyle L. Patwardhan, Ardan Cellular Structure and 3D Bioimaging European Molecular Biology Laboratory (EMBL) European Bioinformatics Institute (EBI) Wellcome Genome Campus Hinxton Cambridgeshire CB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute of New Jersey Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California San Diego La Jolla 92093 CA United States Protein Data Bank in Europe European Molecular Biology Laboratory (EMBL) European Bioinformatics Institute (EBI) Wellcome Genome Campus Hinxton Cambridgeshire CB10 1SD United Kingdom CEITEC Central European Institute of Technology Masaryk University Kamenice 5 Brno 62500 Czech Republic Molecular Biophysics Unit Indian Institute of Science Bangalore India Protein Data Bank Japan Protein Research Foundation Minoh Osaka 562-8686 Japan Protein Data Bank Japan Institute for Protein Research Osaka University Suita Osaka 565-0871 Japan Biological Magnetic Resonance Data Bank Department of Molecular Biology and Biophysics UConn Health 263 Farmington Ave. Farmington 06030-3305 CT United States National Facility for Protein Science Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai 201210 China School of Life Science and Technology Shanghai Tech University Shanghai 201210 China

The Electron Microscopy data Bank (EMDB) is the global public archive of three-dimensional electron microscopy (3DEM) maps of biological specimens derived from transmission electron microscopy experiments. As of 2021, EMDB is managed by the Worldwide Protein data Bank consortium (wwPDB; ***) as a wwPDB Core Archive, and the EMDB team is a core member of the consortium. Today, EMDB houses over 30 000 entries with maps containing macromolecules, complexes, viruses, organelles and cells. Herein, we provide an overview of the rapidly growing EMDB archive, including its current holdings, recent updates, and future plans.

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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

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Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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