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Geoinformatics for Spatial-Infrastructure Development in Earth and Allied Sciences (GIS-IDEAS)

作者： Pongsak Jindasee Sawarin Lerk-U-Suke Jirabhorn Chaiwongsai Krittika Kantawong Napa Rachata Prattana Deeprasertkul Geospatial Data Science School Of Information And Communication Technology University of Phayao Phayao Thailand School Of Information And Communication Technology University of Phayao Phayao Thailand Researcher in Technology and Information Research Section Digital and Technology Development Division Hydro-Informatics Institute (Public Organization) Bangkok Thailand

ISBN: (数字)9798331504243

ISBN: (纸本)9798331504250

Rice production in northern Thailand, particularly in the Mae Na Ruea Sub-district, has been significantly affected by climate change, with increasing temperatures and decreasing water availability leading to reduced yields. This study aims to determine the optimal rice planting periods to maximize yields under changing climatic conditions by using the Decision Support System for Agrotechnology Transfer (DSSAT) model. The research utilizes the CERES-Rice module within DSSAT to simulate rice growth and yields across 15 planting periods from mid-April to late July 2023. Weather, soil, and crop management data were input into the model to predict yields under various environmental conditions. The results indicate that mid-May (W5) is the optimal planting time, with a predicted yield of 4294 kg/ha, 41.95% higher than the average. In contrast, early May (W3) showed the lowest yield at 1889 kg/ha. The simulated yield for weeks (W9) and (W10) were 3,018 kg/ha and 2,987 kg/ha, respectively, which were only 0.23% and 1.26% lower than the average. This demonstrates the model's a high degree of precision. The study highlights the significant impact of planting time on yield outcomes, emphasizing the need for adaptive strategies to cope with climate variability. However, the model's limitations, including underestimation of early-season drought stress and lack of consideration for factors such as irrigation and pest pressures, suggest the need for further research to enhance prediction accuracy and address long-term environmental variability.

关键词： Decision support systems Adaptation models Irrigation Climate variability Predictive models Soil Data models Reliability Stress Meteorology

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research on underwater biological detection method based on multi-scale fusion and attention mechanism 4

Research on underwater biological detection method based on ...

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4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Tang, Longyu Yang, Yunong Wang, Hong College of Computer and Information Science Chongqing Normal University Chongqing China Digital Education Engineering Center for Technology and Applied Research Chongqing Normal University Chongqing China Modern Education Operation Section Chongqing Normal University Chongqing China

ISBN: (纸本)9781450398343

Underwater robots are an important tool for exploiting marine resources, while underwater target detection is a key technology for marine fishing by underwater robots. For the problem that the traditional algorithm is difficult to detect small targets and multiple scales in the underwater environment, this paper proposes an underwater biological detection algorithm based on the multi-scale feature fusion and attention mechanism. First, a YOLOv5 network is constructed, and a bi-directionally weighted BiFPN feature pyramid structure is fed into the neck network so that multiscale target features can be efficiently merged. Second, a channel and spatial convolutional attention mechanism is introduced between the neck network and the prediction network in order to increase the saliency of the underwater target that needs to be detected, which effectively reduces the missed and erroneous detection of underwater targets. The experimental results show that the proposed algorithm can accurately detect multi-scale underwater targets in complex underwater environments with an average accuracy of 80.18%, which is 2.87% better than YOLOv5, and an average recall of 85.06%, which is 4.81% better than YOLOv5. © 2022 ACM.

关键词： Feature extraction

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Assessing the Performance of Machine Learning Models Using Satellite Dataset for Classification of Various Crop Types

Assessing the Performance of Machine Learning Models Using S...

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Geoinformatics for Spatial-Infrastructure Development in Earth and Allied Sciences (GIS-IDEAS)

作者： Montree Pinkaeo Phaisarn Jeefoo Jirabhorn Chaiwongsai Surachai Chantee Pornthep Rojanavasu Prattana Deeprasertkul Geospatial Data Science School Of Information And Communication Technology University of Phayao Phayao Thailand Geographic Information Science School of Information and Communication Technology University of Phayao Phayao Thailand School of Information and Communication Technology University of Phayao Phayao Thailand Technology and Digital Division Hydro-Informatics Institute (Public Organization) Bangkok Thailand Researcher in Technology and Information Research Section Digital and Technology Development Division Hydro-Informatics Institute (Public Organization) Bangkok Thailand

ISBN: (数字)9798331504243

ISBN: (纸本)9798331504250

This paper aims to evaluate machine learning (ML) models' performance, named Random Forest (RF), Gradient Boosting (GB), Artificial Neural Network (ANN), and Support Vector Machine (SVM) for classifying different crop types using images from the Sentinel 2 satellite. The result shows that RF demonstrated the highest values across all criteria, outperforming the other models with an overall accuracy of 0.7365 (+ 0.010). With an accuracy of 0.7183 (+0.016) overall, GB trailed closely behind. The two models demonstrated robust performance, as evidenced by their respective F1-scores of 0.7344 and 0.7161, which suggest a well-balanced compromise between recall and precision. With total accuracies of 0.5429 (+ 0.0024) and 0.5169 (+ 0.017), respectively, the ANN and SVM algorithms performed comparably worse. In comparison to ensemble techniques like RF and GB, this shows that these models might not be as effective for this specific classification problem. The findings of this study can inform decision-making in the selection of ML algorithms for various applications, such as spatial water management, specifically in agriculture and irrigation, agricultural land management, and planning to mitigate the impact of natural disasters in the agricultural sector.

关键词： Support vector machines Radio frequency Accuracy Satellites Image resolution Crops Artificial neural networks Satellite images Classification algorithms Random forests

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On the Principle of Accountability: Challenges for Smart Homes & Cybersecurity1

arXiv

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arXiv 2020年

作者： Urquhart, Lachlan Chen, Jiahong Lecturer in Technology Law University of Edinburgh Visiting Researcher Horizon Digital Economy Research Institute University of Nottingham United Kingdom Research Fellow in Information Technology Law Horizon Digital Economy Research Institute University of Nottingham United Kingdom

This chapter introduces the ‘Accountability Principle’ and its role in data protection (DP) governance. We focus on what accountability means in the context of cybersecurity management in smart homes, considering the EU General Data Protection Law (GDPR) requirements to secure personal data. This discussion sits against the backdrop of two key new developments in data protection law. Firstly, the law is moving into the home, due to narrowing of the so called ‘household exemption’. Concurrently, household occupants may now have legal responsibilities to comply with the GDPR, as they find themselves jointly responsible for compliance, as they are possibly held to determine the means and purposes of data collection with IoT device vendors. As a complex socio-technical space, we consider the interactions between accountability requirements and the competencies of this new class of ‘domestic data controllers’ (DDC). Specifically, we consider the value and limitations of edge-based security analytics to manage smart home cybersecurity risks, reviewing a range of prototypes and studies of their use. We also reflect on interpersonal power dynamics in the domestic setting e.g. device control;existing social practices around privacy and security management in smart homes;and usability issues that may hamper DDCs ability to rely on such solutions. We conclude by reflecting on 1) the need for collective security management in homes and 2) the increasingly complex divisions of responsibility in smart homes between device users, account holders, IoT device/software/firmware vendors, and third parties. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

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A Model Based on RETE Algorithm for Generating Management and Control Measures for Key Personnel of Infectious Diseases 5

A Model Based on RETE Algorithm for Generating Management an...

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5th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Chen, Jinglong Hu, Yao Feng, Xueying Dai, Feng Luan, Yanquan Yuan, Qun Du, Biao Shandong Moses Network Technology Co. Ltd Research and Development Department Yantai China Shandong Center for Disease Control and Prevention Network Information Department Jinan China Nutrition and Food Hygiene Section Yantai Center for Disease Control and Prevention Yantai China Operations Management Section Yantaishan Hospital Yantai China Digital Governance and Infrastructure Development Section Yantai Big Data Bureau Yantai China

ISBN: (纸本)9798331528911

The prevention and control of infectious diseases require the determination of control measures for key personnel. However, in the past, manual research and judgement largely dictated the development of control measures. This approach was inefficient and resulted in a heavy workload, rendering standardization difficult. Furthermore, the traditional generation of control measures often proved unsustainable, curtailing progress towards utilizing an intelligent control system for key personnel. To alleviate the burden on frontline health workers and community disease control staff in implementing control measures, this thesis presents a model for generating intelligent control measures based on the RETE algorithm. Its purpose is to automate the control measures aimed at key individuals with infectious diseases. research has demonstrated that utilizing the algorithm model approach can enhance work efficiency by obtaining control measures that adhere to high standards and accuracy. Subsequently, frontline staff can be relieved from repetitive tasks and focus on ensuring the quality control of the generated control measures and resultant accuracy of the output. © 2024 IEEE.

关键词： information management

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Bridging Data Silos in Oncology with Modular Software for Federated Analysis on Fast Healthcare Interoperability Resources: Multisite Implementation Study

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Journal of Medical Internet research 2025年 27卷 e65681页

作者： Ziegler, Jasmin Erpenbeck, Marcel Pascal Fuchs, Timo Saibold, Anna Volkmer, Paul-Christian Schmidt, Guenter Eicher, Johanna Pallaoro, Peter De Souza Falguera, Renata Aubele, Fabio Hagedorn, Marlien Vansovich, Ekaterina Raffler, Johannes Ringshandl, Stephan Kerscher, Alexander Maurer, Julia Karolin Kühnel, Brigitte Schenkirsch, Gerhard Kampf, Marvin Kapsner, Lorenz A. Ghanbarian, Hadieh Spengler, Helmut Soto-Rey, Iñaki Albashiti, Fady Hellwig, Dirk Ertl, Maximilian Fette, Georg Kraska, Detlef Boeker, Martin Prokosch, Hans-Ulrich Gulden, Christian Medical Center for Information and Communication Technology Universitätsklinikum Erlangen Erlangen Germany Bavarian Cancer Research Center (BZKF) Erlangen Germany Medical Informatics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany Medical Data Integration Center University Hospital Regensburg Regensburg Germany Department of Information Technology University Hospital Regensburg Regensburg Germany Comprehensive Cancer Center Mainfranken University Hospital Würzburg Würzburg Germany Data Integration Center University Hospital Würzburg Würzburg Germany Institute for Artificial Intelligence and Informatics in Medicine Klinikum rechts der Isar School of Medicine and Health Technical University of Munich Munich Germany Data Integration Center Klinikum rechts der Isar School of Medicine and Health Technical University of Munich Munich Germany Section of Precision Psychiatry Clinic for Psychiatry and Psychotherapy Ludwig-Maximilians-Universität München Munich Germany Medical Data Integration Center LMU University Hospital Ludwig-Maximilians-Universität München Munich Germany Digital Medicine University Hospital of Augsburg Augsburg Germany Department of Medicine Data Integration Center Philipps-University Marburg Marburg Germany University Cancer Center Regensburg University Hospital Regensburg Regensburg Germany Comprehensive Cancer Center Munich Klinikum rechts der Isar Technical University of Munich Munich Germany Comprehensive Cancer Center Augsburg University Hospital of Augsburg Augsburg Germany Institute of Radiology Uniklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

Background: Real-world data (RWD) from sources like administrative claims, electronic health records, and cancer registries offer insights into patient populations beyond the tightly regulated environment of randomized controlled trials. To leverage this and to advance cancer research, 6 university hospitals in Bavaria have established a joint research IT infrastructure. Objective: This study aimed to outline the design, implementation, and deployment of a modular data transformation pipeline that transforms oncological RWD into a Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) format and then into a tabular format in preparation for a federated analysis (FA) across the 6 Bavarian Cancer research Center university hospitals. Methods: To harness RWD effectively, we designed a pipeline to convert the oncological basic dataset (oBDS) into HL7 FHIR format and prepare it for FA. The pipeline handles diverse IT infrastructures and systems while maintaining privacy by keeping data decentralized for analysis. To assess the functionality and validity of our implementation, we defined a cohort to address two specific medical research questions. We evaluated our findings by comparing the results of the FA with reports from the Bavarian Cancer Registry and the original data from local tumor documentation systems. Results: We conducted an FA of 17,885 cancer cases from 2021/2022. Breast cancer was the most common diagnosis at 3 sites, prostate cancer ranked in the top 2 at 4 sites, and malignant melanoma was notably prevalent. Gender-specific trends showed larynx and esophagus cancers were more common in males, while breast and thyroid cancers were more frequent in females. Discrepancies between the Bavarian Cancer Registry and our data, such as higher rates of malignant melanoma (3400/63,771, 5.3% vs 1921/17,885, 10.7%) and lower representation of colorectal cancers (8100/63,771, 12.7% vs 1187/17,885, 6.6%) likely result from differences in the time perio

关键词： cancer registries electronic health records federated analysis HL7 FHIR interoperability observational research network oncology real-world data real-world evidence

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research on underwater biological detection method based on multi-scale fusion and attention mechanism 22

Research on underwater biological detection method based on ...

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Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Longyu Tang Yunong Yang Hong Wang College of Computer and Information Science Chongqing Normal University China Digital Education Engineering Center for Technology and Applied Research Chongqing Normal University China Modern Education Operation Section Chongqing Normal University China

ISBN: (纸本)9781450398343

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A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

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Computer Systems Science & Engineering 2023年第8期46卷 1789-1809页

作者： Hicham Moujahid Bouchaib Cherradi Oussama El Gannour Wamda Nagmeldin Abdelzahir Abdelmaboud Mohammed Al-Sarem Lhoussain Bahatti Faisal Saeed Mohammed Hadwan EEIS Laboratory ENSET of MohammediaHassan II University of CasablancaMohammedia28820Morocco STIE Team CRMEF Casablanca-SettatThe Provincial Section of El JadidaEl Jadida24000Morocco Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia College of Computer Science and Engineering Taibah UniversityMedina42353Saudi Arabia Department of Computer Science University of Sheba RegionMarib14400Yemen DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Information Technology College of ComputerQassim UniversityBuraydah 51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaiz6803Yemen

Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of ***,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray *** imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging *** this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected *** model is trained on a dataset containing thousands of X-ray images collected from different *** model was tested and evaluated on an independent *** order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry Group 16(VGG-16)have been implemented using transfer learning *** experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with *** proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging *** finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.

关键词： Artificial intelligence intelligent diagnostic systems decisionmaking COVID-19 convolutional neural network

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MultiplEYE: Creating a multilingual eye-tracking-while-reading corpus 25

MultiplEYE: Creating a multilingual eye-tracking-while-readi...

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Proceedings of the 2025 Symposium on Eye Tracking research and Applications

作者： Deborah Noemie Jakobi Maja Stegenwallner-Schütz Nora Hollenstein Cui Ding Ramune Kaspere Ana Matić Škorić Eva Pavlinusic Vilus Stefan Frank Marie-Luise Müller Kristine M Jensen de López Nik Kharlamov Hanne B. Søndergaard Knudsen Yevgeni Berzak Ella Lion Irina A. Sekerina Cengiz Acarturk Mohd Faizan Ansari Katarzyna Harezlak Pawel Kasprowski Ana Bautista Lisa Beinborn Anna Bondar Antonia Boznou Leah Bradshaw Jana Mara Hofmann Thyra Krosness Not Battesta Soliva Anila Çepani Kristina Cergol Ana Došen Marijan Palmovic Adelina Çerpja Dalí Chirino Jan Chromý Vera Demberg Iza Škrjanec Nazik Dinçtopal Deniz Dr. Inmaculada Fajardo Mariola Giménez-Salvador Xavier Mínguez-López Maroš Filip Zigmunds Freibergs Jéssica Gomes Andreia Janeiro Paula Luegi João Veríssimo Sasho Gramatikov Jana Hasenäcker Alba Haveriku Nelda Kote Muhammad M. Kamal Hanna Kędzierska Dorota Klimek-Jankowska Sara Kosutar Daniel G. Krakowczyk Izabela Krejtz Marta Łockiewicz Kaidi Lõo Jurgita Motiejūnienė Jamal A. Nasir Johanne Sofie Krog Nedergård Ayşegül Özkan Mikuláš Preininger Loredana Pungă David Robert Reich Chiara Tschirner Špela Rot Andreas Säuberli Jordi Solé-Casals Ekaterina Strati Igor Svoboda Evis Trandafili Spyridoula Varlokosta Mila Vulchanova Lena A. Jäger Department of Computational Linguistics University of Zurich Zurich Switzerland Humboldt-University of Berlin Berlin Germany and University of Koblenz Koblenz Germany Kaunas University of Technology Kaunas Lithuania Faculty of Education and Rehabilitation Sciences Dept of Speech and Language pathology Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Centre for Language Studies Radboud University Nijmegen Netherlands ZPID Leibnitz Institute for Psychology Trier Germany Aalborg University Aalborg Denmark Data and Decision Sciences Technion Haifa Israel Department of Psychology College of Staten Island Staten Island New York USA Cognitive Science Department Jagiellonian University Krakow Poland Silesian University of Technology Gliwice Poland Institute of Informatics Silesian University of Technology Gliwice Poland Basque Center on Cognition Brain & Language Donostia Spain Human-Centered Data Science University of Göttingen Göttingen Germany Department of Computational Linguistics University of Zurich Zurich Switzerland and Digital Society Initiative University of Zurich Zurich Switzerland Lab of Psycholinguistics & Neurolinguistics National and Kapodistrian University of Athens Athens Greece Department of Computational Linguistics University of Zurich University of Zurich Switzerland University of Zurich Zurich Switzerland University of Tirana Tirana Albania Faculty of Teacher Education University of Zagreb Zagreb Croatia Hrvatska Croatia and Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia University of Zagreb Zagreb Croatia Dept. of Speech & Lang. Path. Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia Academy of Sciences of Albania Tirana Albania Radboud University Nijmegen Netherlands Institute of Czech Language and Theory of Communication Faculty of Arts Charles University Prague Czech Republic Saarland Universit

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A reliable neural network framework for the Zika system based reservoirs and human movement

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Knowledge-Based Systems 2024年 292卷

作者： Sabir, Zulqurnain Khan, Sundas Naqeeb Raja, Muhammad Asif Zahoor Babatin, M.M. Hashem, Atef F. Abdelkawy, M.A. Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Graphics Computer Vision and Digital Systems Silesian University of Technology Gliwice Poland Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Riyadh11566 Saudi Arabia Department of Mathematics and Information Science Faculty of Science Beni-Suef University Egypt

The motive of current investigations provides the numerical solutions of the neuro computing solver based on the Levenberg-Marquardt backpropagation neural network approach (LMB) to solve the Zika virus system of reservoirs and human movement. The mathematical form of the human movement model is based on ten different classes, which makes the model nonlinear. The solution of this nonlinear mathematical model is presented by using the stochastic LMB neural network process. A dataset is designed based on the Adam solver in order to reduce the mean square error by dividing the data as training, endorsement, and testing with the use of 75%, 10% and 15%. Ten numbers of neurons have been taken by using the log-sigmoid transfer in the hidden layer for solving the human movement model. The precision of the proposed solver is authenticated by using the valuation of reference and proficient outcomes, while the reducible absolute error presents the correctness of the scheme. Moreover, the statistical performances based different proportional schemes have been used to authenticate the reliability of the scheme. © 2024 Elsevier B.V.

关键词： Neural networks

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