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Joint Conference of the 6th Annual Conference of Industrial and System engineering 2019, ACISE 2019 and 1st International Conference on Risk management as an Interdisciplinary Approach 2019, ICRMIA 2019

作者： Fajar, A.N. Shofi, I.M. Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia Informatics Engineering UIN Syarif Hidayatullah Indonesia

The objective of this study is to create a services design of E-Government system that can help Indonesian government to resolve some problems such as diversity platform and application. It causes the island applications that can make decision support processes is not well happen. In this study, we use literature review as an approach to discuss the phenomenon. It is performed by identifying, evaluating, and combining information from several other studies which relevant with this study's research questions. We use services oriented analysis and design approach to discussing the phenomenon. The ability of Service-oriented Architecture to connect multiple services between diversity systems and it can help the enterprise application travelling in Indonesian government. The case in this study is travelling system in Indonesia Government. The results in this study are the conceptual services for Travelling System using SOA approach and ESB Technology. It consists of several services and business services which is can be integrated among of them. © Published under licence by IOP Publishing Ltd.

关键词： Information services

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Research on Data Security and Privacy of Smart Grids

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International Journal of Network Security 2024年第5期26卷 901-910页

作者： Hwang, Min-Shiang Chang, Yung-Ling Lin, Ko-Yu Lin, Iuon-Chang Yang, Cheng-Ying Department of Computer Science & Information Engineering Asia University 500 Lioufeng Rd. Wufeng Taichung Taichung41354 Taiwan Fintech and Blockchain Research Center Asia University 500 Lioufeng Rd. Wufeng Taichung Taichung41354 Taiwan The Ph.D. Program in Artificial Intelligence Asia University Taiwan Department of Management Information Systems National Chung Hsing University 500 Lioufeng Rd. Wufeng Taichung Taichung41354 Taiwan Department of Computer Science University of Taipei Taipei Taiwan

Developing a smart grid is a trend nowadays and an indispensable basic necessity of life. In recent years, the electricity demand has risen rapidly due to economic development and quality of life improvement. The power grid in significant cities is unstable due to old equipment, and power outages occur frequently. The smart grid can adjust the grid’s distribution strategy through the consumer’s real-time electricity demand so that the consumer’s electricity consumption data is always transmitted to the grid. Failure to protect the privacy and security of such information will expose consumers’ habits and even make the grid unnecessarily wasteful. The smart grid’s abnormality detection efficiency determines the overall grid’s stability. Failure to detect anomalies or power theft in the grid promptly will not only lead to the selection of the wrong distribution strategy for the smart grid but also reduce the transmission efficiency of the grid. This research ensures the privacy and security of transmitted data to enhance the efficiency of abnormality detection in the smart grid. The research issues are divided into privacy of transmitted data, data security authentication, and anomaly detection efficiency. © (2024), (International Journal of Network Security). All rights reserved.

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Correcting Confounding Canopy Structure, Biochemistry and Soil Background Effects Improves Leaf Area Index Estimates Across Diverse Ecosystems from Sentinel-2 Imagery

SSRN

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SSRN 2024年

作者： Wan, Liang Ryu, Youngryel Dechant, Benjamin Hwang, Yorum Feng, Huaize Kang, Yanghui Jeong, Sungchan Lee, Jeongho Choi, Changhyun Bae, Joonhwan Research Institute of Agriculture and Life Sciences Seoul National University Seoul Korea Republic of Interdisciplinary Program in Agricultural and Forest Meteorology Seoul National University Seoul Korea Republic of Department of Landscape Architecture and Rural Systems Engineering Seoul National University Seoul Korea Republic of Halle-Jena-Leipzig Leipzig Germany Leipzig University Leipzig Germany Department of Earth and Environment Boston University BostonMA United States Department of Environmental Science Policy and Management University of California Berkeley BerkeleyCA94720 United States

High-resolution leaf area index (LAI) data are essential for sustainable agro-ecosystem management and precise disturbance detection. Previous LAI products were primarily derived from satellite data with limited spatiotemporal or spectral resolutions, which could be overcome with the use of Sentinel-2. While hybrid methods that integrate PROSAIL simulations with machine learning offer advantages in extracting high-resolution LAI from Sentinel-2, they still face challenges due to confounding factors related to canopy structure, leaf biochemistry, and soil background. To reduce impacts of these confounders, we developed an efficient hybrid method for Sentinel-2-based LAI retrieval. Our approach consists of random forest models trained on simulated datasets generated by PROSAIL-5B with two refinements: variable canopy fraction of senescent leaves (FS) and soil bidirectional reflectance factor (BRF) simulated by Brightness-Shape-Moisture (BSM) model. We corrected canopy BRF using near-infrared reflectance of vegetation (NIRV) and vegetation cover within mixed pixels (VC). For validation, we used LAI measurements across different vegetation types from the Copernicus Ground Based Observations for Validation (GBOV) and Korea flux (KoFlux) sites during 2019–2023. Our results showed that coupling BSM and FS with PROSAIL-5B simulations improved hybrid LAI estimates, reducing RMSE by 10.8%–73.8%. Utilizing NIRV and VC to correct canopy BRF better quantified LAI in most vegetation types, with RMSE reduced by 15.3%–64.8%. Our hybrid method showed robust agreement with ground validation LAI data from GBOV (R2 = 0.88, RMSE = 0.71) and KoFlux (R2 = 0.81, RMSE = 0.73) datasets. Overall, our method (R2 = 0.58–0.93) outperformed both the benchmark Sentinel Application Platform (R2 = 0.11–0.85) and data-driven (R2 = 0.09–0.85) algorithms in producing precise seasonal LAI data at finer resolutions. Our findings underscore the potential of the proposed hybrid method for high-resolution L

关键词： Vegetation

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Deep Learning-based Integrated System for Intraoperative Blood Loss Quantification in Surgical Sponges

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IEEE Journal of Biomedical and Health Informatics 2024年 PP卷 PP页

作者： Nguyen, Dang Le, Minh Huu Nhat Huynh, Phat K. Le, Trung Q. Charles-Okezie, Chinyere Diaz, Michael J. Sabet, Cameron Dang, Hung The Nguyen, Tuan Nguyen, Hoan Tran, Minh Le, Nguyen Quoc Khanh Muncey, Aaron Taipei Medical University International Ph.D. Program in Medicine College of Medicine Taipei110 Taiwan University of South Florida Department of Medical Engineering TampaFL United States University of South Florida Department of Industrial and Management Systems Engineering TampaFL United States Georgetown University School of Medicine Department of Medicine WashingtonDC United States Faculty of Life Sciences & Medicine King's College London School of Biomedical Engineering & Imaging Sciences London United Kingdom Minerva University San FranciscoCA United States Fredericton Leo Hayes High School NB Canada Taipei Medical University Research Center for Artificial Intelligence in Medicine Taipei106 Taiwan Taipei Medical University Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei110 Taiwan Taipei Medical University Hospital Translational Imaging Research Center Taipei110 Taiwan Moffitt Cancer Center and Research Institute Department of Anesthesiology TampaFL United States

Accurate quantification of intraoperative blood loss is crucial for enhancing patient safety and the success rate of surgeries. Traditional estimation techniques, mainly reliant on visual assessments, are prone to significant inaccuracies due to their subjective nature. This study introduces MDCare, an innovative deep learning-integrated system designed to substantially improve the precision of blood loss quantification using surgical sponges. By integrating advanced hardware components, including a mass sensor and webcam, with sophisticated algorithms like ResNet-18 and YOLOv4, MDCare achieves classification accuracy up to 96.2% and sponge detection accuracies above 91% for both synthetic and real blood scenarios. The system processes images at 7.4 frames per second, aligning with the exigent pace of surgical environments, thereby supporting surgeons with real-time, accurate blood loss data essential for timely and informed decision-making. The contributions of the paper are: (1) Demonstrating the application of advanced machine learning models in a critical clinical setting, achieving significantly higher accuracy in blood loss estimation compared to traditional methods;(2) Validating the system's efficacy in real-time surgical environments, thereby enhancing the decision-making process and potentially reducing postoperative complications;(3) Setting a new standard in surgical care by integrating a complex system into real-world clinical workflows, showcasing its adaptability and potential for widespread adoption. Future work will focus on expanding the dataset and refining the algorithms to ensure the MDCare system's robustness and adaptability across surgical settings. The findings underscore the potential of MDCare to automate and refine critical aspects of surgery, marking a significant advancement in surgical care. © 2024 IEEE.

关键词： Digital storage

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IoT Attack Detection and Prevention Through Machine Learning System

IoT Attack Detection and Prevention Through Machine Learning...

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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Kanishk Agrawal Pavan Kumar Reddy Poli Yash Gupta Garv Juneja Adhip Kishen Bopaiah Dinesh Ashok Arani Department of IET CSE Manglayatan University Aligarh Uttar Pradesh India Managing Information Technology Implementations Program management for fortune 6 Healthcare Company USA Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani India Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology Guru Gobind Singh Indraprastha University Delhi India ICT Trainer Trinity College Bangalore India Manglayatan University Aligarh Uttar Pradesh India

Attacks on web applications are constantly growing in both frequency and severity. Abundant data on the internet stimulates hackers to attempt different types of cyberattacks. Attack detection using conventional approaches and outdated data processing techniques has become outmoded as a result of this development. The purpose of this study is to investigate IoT attacks and discuss the efficient ML technique implementation strategies for restricting security risks. Among different security techniques, Machine learning (ML) systems demonstrated commendable feasibility in improving network and device security for the Internet of Things. The study with contextual research recognises and comprehends that modified “Support Vector Machine (SVM)” as well as “Random Forest (RF)” ML techniques showed optimal performance in IoT attack detection and prevention.

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Influences of key factors on river water quality in urban and rural areas: A review

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Case Studies in Chemical and Environmental engineering 2023年 8卷

作者： Anh, Nguyen Tuan Can, Le Duy Nhan, Nguyen Thi Schmalz, Britta Luu, Tran Le Doctoral Training Program in Sustainable Urban Development Vietnamese German University Viet Nam Nong Lam University of Ho Chi Minh City Gia Lai Campus Viet Nam Department of Mechatronics and Sensor Systems Technology Vietnamese German University Vanh Dai 4 Quarter 4 Thoi Hoa Ward Ben Cat Binh Duong Province Viet Nam Master Program in Water Technology Reuse and Management Vietnamese German University Vanh Dai 4 Quarter 4 Thoi Hoa Ward Ben Cat Binh Duong Province Viet Nam Chair of Engineering Hydrology and Water Management Technical University of Darmstadt Germany

Water quality in rivers is deteriorating in urban and rural areas due to natural and anthropogenic factors. Understanding how changes and factors affect river water quality is crucial for managing water quality in river basins. This review focuses on analyzing key factors affecting water quality, and the temporal and spatial variations of water quality in rivers flowing in rural and urban areas. Natural processes such as weathering of rocks, evapotranspiration, atmospheric deposition, climate change, and natural disasters cause changes in the quality of river water. Anthropogenic factors could stem from industrial effluents, domestic activities, and agricultural activities such as the application of fertilizers, manures, pesticides, animal husbandry activities, irrigation practices, deforestation, and aquaculture. The seasonal variations in river water quality are discussed, and land use or cover could affect water quality parameters in a negative or positive way. In addition to traditional contaminants such as biodegradable organic matter, heavy metals, and pathogens, emerging and persistent pollutants such as organochlorine pesticides (OCPs), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), perfluoroalkyl and polyfluoroalkyl substances (PFASs), and phamaceutic active compounds (PhACs) has been found in many rivers, which could pose a threat to human and animal health. The comparison of key factors and parameters in urban and rural areas is also clarified, which provides authorities and policymakers with a deep understanding and supports decision-making in sustainable water management. © 2023 The Authors

关键词： River basin River water quality Rural areas Urban areas Water pollution

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Citizen science in environmental and ecological sciences

NATURE REVIEWS METHODS PRIMERS

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NATURE REVIEWS METHODS PRIMERS 2022年第1期2卷 1-20页

作者： Morneau, Dominique Novel Data Ecosystems for Sustainability Research Group Advancing Systems Analysis Program International Institute for Applied Systems Analysis (IIASA) Laxenburg Austria Centre d’écologie et des sciences de la conservation Muséum national d’Histoire naturelle Paris France Faculté de droit Université de Namur Namur Belgium Citizen Science Lab Leiden University Leiden Netherlands Department of Mechanical Engineering University of Bath Bath UK Nordic Foundation for Development and Ecology (NORDECO) Copenhagen Denmark Department of Biology Brandeis University Waltham MA USA Ornamental Plant Pathology Department of Plant Pathology Puyallup Research and Extension Center Washington State University Puyallup WA USA Environmental and Sustainability Participatory Information Systems Group Institute of Marine Sciences (ICM-CSIC) Barcelona Spain Electron Microscopy Science Technology Platform The Francis Crick Institute London UK Departamento de Biologia Marina Facultad Ciencias del Mar Universidad Católica del Norte Coquimbo Chile Millennium Nucleus Ecology and Sustainable Management of Oceanic Island (ESMOI) Coquimbo Chile Centro de Estudios Avanzados en Zonas Áridas (CEAZA) Coquimbo Chile Department of Geography University College London (UCL) London UK

This PrimeView highlights the utility of citizen science across a range of ecological and environmental science research.

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Using Online Grocery Applications during the COVID-19 Pandemic: Their Relationship with Open Innovation

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Journal of Open Innovation: Technology, Market, and Complexity 2022年第2期8卷

作者： Gumasing, Ma. Janice J. Prasetyo, Yogi Tri Persada, Satria Fadil Ong, Ardvin Kester S. Young, Michael Nayat Nadlifatin, Reny Redi, Anak Agung Ngurah Perwira School of Industrial Engineering and Engineering Management Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines School of Graduate Studies Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Malang 65154 Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Industrial Engineering Department BINUS Graduate Program—Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

This present research examines the behavioral intentions of Filipinos to use online grocery applications during the novel COVID-19 pandemic. The study proposes an integration of the health belief model (HBM) and the Unified Theory of Acceptance and Use of Technology (UTAUT2) to identify the factors affecting the acceptance and usage of Filipinos of online grocery applications in terms of the impact of health risk for COVID-19. To accurately measure the factors and their relationship to behavioral intentions and usage behavior, a questionnaire was developed and distributed to 373 residents in the Philippines. Partial least squares structural equation modeling (PLS-SEM) was applied as an analytical method for this study. The results revealed that performance expectancy, perceived benefits, perceived severity, and cues to action significantly influenced the behavioral intentions and usage of online grocery apps during the COVID-19 pandemic. The study’s findings can be utilized as a theoretical framework for future researchers of consumer behavior;e-commerce developers;and grocery industry retailers, to enhance the innovation and services of online grocery applications. The results of this study may also be used and capitalized on by investors and managers to apply in strategizing when developing and marketing online grocery applications among con-sumers. Moreover, the framework of this study may be adopted and utilized by other online markets, even in different counties. Further theoretical and practical aspects are discussed in this paper. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： health belief model online grocery applications structural equation modeling UTAUT2

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Advancing Precision Aquaculture Through Big Data Analytics and Machine Learning in Canadian Fish Farming

Advancing Precision Aquaculture Through Big Data Analytics a...

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OCEANS

作者： Francisco Bravo Joana Amorim Melika Besharati Amirkandeh Peter Bodorik Vitor Cerqueira Nuno R.C. Gomes Jennie Korus Mariana Oliveira Marianne Parent João Pimentel Derek Reilly Tyler Sclodnick Jon Grant Ramón Filgueira Christopher Whidden Luis Torgo Facultad de Ingenieria y Ciencias Universidad Adolfo Ibañez Santiago Chile Faculty of Computer Science Dalhousie University Halifax Canada Faculty of Engineering University of Porto Portugal Institut d 'Estudis Espacials de Catalunya (IEEC) Barcelona Spain Innovasea Marine Systems Bedford Canada Department of Health Management and Centre for Veterinary Epidemiological Research (CVER) Atlantic Veterinary College University of Prince Edward Island Charlottetown Canada Department of Oceanography Dalhousie University Halifax Canada Marine Affairs Program Dalhousie University HalifaxZ Canada Deceased

ISBN: (数字)9798331540081

ISBN: (纸本)9798331540098

The aquaculture industry faces significant challenges related to sustainability, productivity, and fish welfare. Key issues include managing environmental conditions, disease, pests, and data integration from various sensors and monitoring systems. The BigFish project aims to address these challenges through advanced analytics and machine learning, focusing on three case studies in Atlantic salmon farms: predicting oxygen levels, reducing sea lice infestations, and improving data interaction and visualization. Predictive models for oxygen levels and sea lice infestation, as well as natural language interfaces for data visualization, demonstrate the potential for improved decision-making and management practices in aquaculture. Early results indicate the effectiveness of these approaches, highlighting the importance of data-driven solutions in enhancing industry sustainability and productivity.

关键词： Productivity Oceans Data visualization Machine learning Predictive models Fish Sensor systems Sensors Sustainable development Aquaculture

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The Evaluation of the Local Beer Industry during the COVID-19 Pandemic and Its Relationship with Open Innovation

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Journal of Open Innovation: Technology, Market, and Complexity 2022年第3期8卷

作者： Ong, Ardvin Kester S. Pequeña, Arianne R. Prasetyo, Yogi Tri Chuenyindee, Thanatorn Buaphiban, Thapanat Persada, Satria Fadil Nadlifatin, Reny School of Industrial Engineering and Engineering Management Mapua University 658 Muralla St. Intramuros Manila 1102 Philippines School of Graduate Studies Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines Department of Industrial Engineering and Management Yuan Ze University 135 Yuan-Tung Road Chung-Li 32003 Taiwan Department of Industrial Engineering and Aviation Management Navaminda Kasatriyadhiraj Royal Air Force Academy Bangkok 10220 Thailand BINUS Business School Undergraduate Program Entrepreneurship Department Bina Nusantara University Jakarta 11480 Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia

The beer industry is one of the businesses affected during the COVID-19 pandemic. Despite the exponential growth of the beer industry throughout the years, this aspect of the beverage industry has gained limited attention and has been underexplored. This study aimed to provide a better and up-to-date understanding of Philippine-beer consumers to speed up its recovery. An online survey with 853 volunteer respondents was conducted to investigate Filipinos’ local beer consumption considering frequency, intake, expenses, and preference. A descriptive analysis of the consumers’ self-perceived evaluation of the changes in drinking showed a slight decrease in frequency, intake, and expenses and a minor change in preference. Somers’ d and the chi-squared test results indicated significant relationships between each demographic information (age, sex, and income) and frequency, intake, and expenses. In addition, a conjoint analysis with an orthogonal design indicated that price was the most important attribute (58.025%), followed by primary taste (12.452%), alcohol content (9.706%), mouthfeel (6.445%), aftertaste (6.355%), aroma (5.189%), and, lastly, color (1.827%). The findings of this study could be used as a baseline for improved product offerings, customized advertisements, and market segmentation. Moreover, the results of this study could be applied and extended by breweries to promote and create strategies. Lastly, this study could be extended and utilized by other beverage industries worldwide. © 2022 by the authors.

关键词： beer conjoint analysis consumer preference COVID-19 pandemic expenditure

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