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International Conference on Information Technology Systems and Innovation (ICITSI)

作者： Aniessa Rinny Asnaning Septafiansyah Dwi Putra Land Source and Environment Engineering Study Program Management of Informatics Study Program Politeknik Negeri Lampung Bandar Lampung Indonesia

ISBN: (纸本)9781538656945;9781538656938

Flood is one of the disasters that have a big impact in Indonesia. Flooding can affect social, economic, agricultural aspects, etc. Flood disaster is a difficult thing to avoid, the best approach is to reduce the impact caused. Research on flood mitigation began to move to an artificial intelligence approach to providing information on early warning systems (EWS). EWS is a series of systems that function to inform of the occurrence of natural events in the form of floods that will notify the disaster-related floods that will occur. This research will build an intelligent system for building the EWS system. A smart AWS certainly requires various sensors to collect data for later analysis. One of the most important sensors to the construction of the EWS system is the automatic water level recorder (AWLR) sensor. In this paper will specifically discuss the design stages of the AWLR in conducting water level monitoring at the water-gate dam. The function of AWLR sensor is for monitoring and recording in a database with real-time sensing. The results of this research show that the low-cost AWLR sensor has reduced processing time by 92.7% compared to conventional data processing.

关键词： Floods Monitoring Logic gates Real-time systems Time measurement Manuals Rivers

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A Study on Medicinal Plant Leaf Recognition Using Artificial Intelligence 3

A Study on Medicinal Plant Leaf Recognition Using Artificial...

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3rd International Conference on informatics, Multimedia, Cyber, and Information System, ICIMCIS 2021

作者： Ayumi, Vina Ermatita, Ermatita Abdiansah, Abdiansah Noprisson, Handrie Purba, Mariana Utami, Marissa Universitas Sriwijaya Doctoral Program in Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia Universitas Mercu Buana Faculty of Computer Science Jakarta Indonesia Program of Informatics Management Politeknik Anika Palembang Indonesia Muhammadiyah University of Bengkulu Faculty of Engineering Bengkulu Indonesia

ISBN: (纸本)9781665427333

Medicinal plant recognition manually takes a lot of time and money. Moreover, to reduce these resources, some researchers propose to implement artificial intelligence technology. This paper aims are to conduct a systematic literature review of medicinal plant leaf recognition published in the last two years (2019-2020) from IEEE, Springer and Science Direct. We obtained 15 studies in the field of medicinal plant leaf recognition using artificial intelligence. The dataset used for medicinal plant leaf recognition is mostly used private dataset, however, there are public dataset named Leaf, Flavia, Swedish dataset. We also found robust method that can be used for medicinal plant leaf recognition is Multichannel Modified Local Gradient Pattern (MCMLGP) and Gray Level Co-Occurrence Matrix (GLCM) as feature extraction;and Convolutional Neural Network (CNN), Multi-Layer Perceptron trained with Backpropagation algorithm (MLP-BP), Support Vector Machine (SVM), and Transfer Learning (VGG19) as classifier. © 2021 IEEE.

关键词： Image processing

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Meteorological influences on dengue transmission in Pakistan

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Asian Pacific Journal of Tropical Medicine 2016年第10期9卷 932-938页

作者： Suleman Atique Shabbir Syed Abdul Chien-Yeh Hsu Ting-Wu Chuang Graduate Institute of Biomedical Informatics College of Medical Science and TechnologyTaipei Medical University Master Program in Global Health and Development Taipei Medical University Department of Information Management National Taipei University of Nursing and Health Sciences Department of Molecular Parasitology and Tropical Diseases School of MedicineCollege of MedicineTaipei Medical University

Objective: To identify the influences of local and regional climate phenomena on dengue transmission in Lahore District of Pakistan, from 2006 to 2014. Methods: Time-series models were applied to analyze associations between reported cases of dengue and climatic parameters. The coherence trend of regional climate phenomena(IOD and ENSO) was evaluated with wavelet analysis. Results: The minimum temperature 4 months before the dengue outbreak played the most important role in the Lahore District(P=0.03). A NINO 3.4 index 9 months before the outbreaks exhibited a significant negative effect on dengue transmission(P=0.02). The IOD exhibited a synchronized pattern with dengue outbreak from 2010 to 2012. The ENSO effect(NINO 3.4 index) might have played a more important role after 2012. Conclusions: This study provides preliminary results of climate influences on dengue transmission in the Lahore District of Pakistan. An increasing dengue transmission risk accompanied by frequent climate changes should be noted. Integrating the influences of climate variability into disease prevention strategies should be considered by public health authorities.

关键词： Dengue Fever Pakistan Climate IOD NINO 3.4

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The Banking Industry is Undergoing a Digital Transformation as Part of Industrial 4.0 10

The Banking Industry is Undergoing a Digital Transformation ...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Sardjono, Wahyu Pranata, Dewi Sagita Ramdhana, Aditya Agung Dwi BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia Mathematical & Engineering Sciences King's College London Cyber Security Studi Program Faculty of Natural Department of Informatics West Minster Strands WC2R 2LS United Kingdom University of Airlangga Master of Management Faculty of Economics and Business Surabaya60286 Indonesia

ISBN: (纸本)9798350363104

In the current day, digital transformation is imperative;both customer demand and market competitiveness are mostly driving that trend. The fourth industrial revolution has also affected the financial sector. One difficulty that has to be addressed is the desire from customers for quick and useful services. Traditional procedures need to be replaced with digital ones. Furthermore, banking organizations will have a competitive edge in the current market if they are successful in this digital transformation process. Successful transformation, however, does not happen by accident;in fact, the likelihood of completing this change is seen to be very low, necessitating rigorous planning for a company that is ready for the future. There are several components to the preparation in question, both internal and external. In this situation, the company's ability to combine and rearrange its resources quickly is essential for adapting to the ever-changing business environment. © 2024 IEEE.

关键词： Commerce

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Hybrid neural systems for large scale credit risk assessment applications

Hybrid neural systems for large scale credit risk assessment...

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作者： De Amorim, B.P. Vasconcelos, G.C. Brasil, L.M. Center for Informatics - CIn Federal University of Pernambuco UFPE Recife PE Brazil Post-Graduation Program in Knowledge Management and Information Technology Catholic University of Brasilia UCB Brasília DF Brazil

Hybrid Neural Systems that integrate symbolic algorithms or fuzzysystems to Artificial Neural Networks (ANN) are a potential alternativeto the more traditional ANN models. However, in contrast with the ANNmodels, these systems have not been yet fully explored from a practicalviewpoint to show their effectiveness in large scale applications. Thispaper presents an extensive comparative analysis of the neuro-fuzzymodels FWD (Feature-Weighted Detector) and FuNN (Fuzzy Neural Network),together with their rule extraction techniques in a large-scale *** aspects are considered: generalization performance of the models,and the interpretation and explanation qualities of the extractedknowledge. The experiments are conducted in the context of a large scalecredit risk assessment application in a real-world operation of aBrazilian financial institution. The results attained are compared tothose observed with multi-layer perceptron networks. © 2007 - IOS Press and the authors. All rights reserved.

关键词： Neural networks

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Control Method of Effect of Robust Optimization in Multi-Player Multi-Objective Decision-Making

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American Journal of Operations Research 2019年第4期9卷 175-191页

作者： Tomoaki Yatsuka Aya Ishigaki Yuki Kinoshita Tetsuo Yamada Masato Inoue Department of Industrial Administration Graduate School of Science and Technology Tokyo University of Science Chiba Japan Management Science and Social Informatics Program Department of Informatics Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan Department of Mechanical Engineering Informatics Meiji University Kanagawa Japan

In the real situations of supply chain, there are different parts such as facilities, logistics warehouses and retail stores and they handle common kinds of products. In this research, these situations are focused on as the background of this research. They deal with the common quantities of their products, but due to their different environments, the optimal production quantity of one part can be unacceptable to another part and it may suffer a heavy loss. To avoid that kind of unacceptable situations, the common production quantities should be acceptable to all parts in one supply chain. Therefore, the motivation of this research is the necessity of the method to find the production quantities that make all decision makers acceptable is needed. However, it is difficult to find the production quantities that make all decision makers acceptable. Moreover, their acceptable ranges do not always have common ranges. In the decision making of car design, there are similar situations to this type of decision making. The performance of a car consists of purposes such as fuel efficiency, size and so on. Improving one purpose makes another worse and the relationship between these purposes is tradeoff. In these cases, Suriawase process is applied. This process consists of negotiations and reviews of the requirements of the purposes. In the step of negotiations, the requirements of the purposes are share among all decision makers and the solution that makes them as satisfied as possible. In the step of reviews of the requirements, they are reviewed based on the result of the negotiation if the result is unacceptable to some of decision makers. Therefore, through the iterations of the two steps, the solution that makes all decision makers satisfied is obtained. However, in the previous research, the effects that one decision maker reviews requirements in Suriawase process are quantified, but the mathematical model to modify the ranges of production quantities of all decision ma

关键词： Linear Physical programming Suriawase Process Multi-Player Decision-Making Supply Chain Coordination Robust Optimization

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Air quality assessment from social media and structured data: Pollutants and health impacts in urban planning 32

Air quality assessment from social media and structured data...

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32nd IEEE International Conference on Data Engineering Workshops, ICDEW 2016

作者： Du, Xu Emebo, Onyeka Varde, Aparna Tandon, Niket Chowdhury, Sreyasi Nag Weikum, Gerhard Environmental Management Program Montclair State University MontclairNJ United States Department of Computer Science Montclair State University MontclairNJ United States Databases and Information Systems Max Planck Institute for Informatics Saarbruecken Germany

ISBN: (纸本)9781509021086

This paper describes our work on mining pollutant data to assess air quality in urban areas. Notable aspects of this work are that we mine social media and structured data in a domain-specific context, incorporate commonsense knowledge in mining media opinions and focus on the urban planning domain in a multicity environment. The results of mining are useful for predictive analysis in urbanization. A significant contribution is that we provide useful information on urban health impacts. © 2016 IEEE.

关键词： Urban planning

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A Consensus Privacy Metrics Framework for Synthetic Data

arXiv

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arXiv 2025年

作者： Pilgram, Lisa Dankar, Fida K. Drechsler, Jörg Elliot, Mark Domingo-Ferrer, Josep Francis, Paul Kantarcioglu, Murat Kong, Linglong Malin, Bradley Muralidhar, Krishnamurty Myles, Puja Prasser, Fabian Raisaro, Jean Louis Yan, Chao El Emam, Khaled School of Epidemiology and Public Health University of Ottawa ON Canada CHEO Research Institute ON Canada Department of Nephrology and Medical Intensive Care Charité – Universitätsmedizin Berlin Berlin Germany Department for Statistical Methods Institute for Employment Research Nuernberg Germany Institute for Statistics Ludwig-Maximilians-Universität Munich Germany Joint Program in Survey Methodology University of Maryland United States The Cathie Marsh Institute Research School of Social Sciences University of Manchester Manchester United Kingdom Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Tarragona Spain Max Planck Institute for Software Systems Germany Department of Computer Science Virginia Tech United States Department of Mathematical and Statistical Sciences University of Alberta Alberta Canada Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Marketing and Supply Chain Management University of Oklahoma Oklahoma United States Medicines and Healthcare products Regulatory Agency London United Kingdom Berlin Institute of Health at Charité Universitätsmedizin Berlin Medical Informatics Group Berlin Germany Biomedical Data Science Center University Hospital Lausanne Lausanne Switzerland

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals’ privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data. © 2025, CC BY.

关键词： Differential privacy

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Spatial dynamics of dengue fever spreading for the coexistence of two serotypes with an application to the city of Sao Paulo, Brazil

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COMPUTER METHODS AND programS IN BIOMEDICINE 2022年 219卷 106758-106758页

作者： Pereira, F. M. M. Schimit, P. H. T. Informatics and Knowledge Management Graduate Program Universidade Nove de Julho Rua Vergueiro 235/249 São Paulo 01525-000 SP Brazil

Background and objective: Dengue fever is a disease in which individuals' spatial distribution and Aedes aegypti mosquitoes breeding places are important factors for the disease dynamics. Typically urban, dengue is a problem for least developed countries due to the ineffectiveness in controlling the vector and disorderly urbanization processes. The result is a composition of urban sanitation problems and areas with high demographic densities and intense flows of people. This paper explores the spatial distribution of vector breeding places to evaluate introducing a new dengue serotype to a population at equilibrium for a pre-existing serotype. The paper's objective is to analyze the spatial dynamics of dengue using variations of the basic reproduction ***: A model based on probabilistic cellular automata is proposed to permitting the necessary flexibility to consider some spatial distributions of vector breeding places. Then, ordinary differential equations are used as a mean-field approach of the model, and the basic reproduction number ( R 0 ) is derived considering the next-generation matrix method. A spatial approach for R 0 is also proposed, and the model is tested in a neighbourhood from the city of Sao Paulo, Brazil, to examine the potential risks of vector breeding cells ***: The results indicated that the more spread out these places, the higher are the values of R 0 . When the model is applied to a neighbourhood in Sao Paulo, residential areas may boost the infections and must be under public vigilance to combat vector breeding ***: Considering the mean-field approximation of the cellular automata model by ordinary differential equations, the basic reproduction number derived returned an estimative of the disease dynamics in the population. However, the spatial basic reproduction number was more assertive in showing areas with a higher disease incidence. Moreover, the model could be easily adapted to be used in rea

关键词： Basic reproduction number Dengue Probabilistic cellular automata Spatial basic reproduction number Spatial dynamics

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An Examination of the Possible Application of Artificial Intelligence Audit Process in Organizations 6

An Examination of the Possible Application of Artificial Int...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Sardjono, Wahyu Pranata, Dewi Sagita Isamesal, Muhammad Information Systems Management Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Cyber Security Studi Program Department of Informatics Faculty of Natural Mathematical & Engineering Sciences King’s College London West Minster WC2R 2LS United Kingdom Management Department Faculty of Economics and Busines Universitas of Airlangga Surabaya10430 Indonesia

ISBN: (纸本)9798331539603

This study investigates how artificial intelligence (AI) can transform audit processes, including how AI can be used in businesses to execute non-technical audit tasks and what effect this will have on those tasks. A qualitative case study methodology is used in the research design to give detailed insights into how AI is being adopted by businesses. This study aims to analyze the aspects that impact AI implementation from the viewpoints of managers, auditors, and other stakeholders. The findings show that artificial intelligence (AI) has a great deal of promise for non-technical audit procedures in businesses. AI can automate tedious jobs, evaluate large data sets to identify hazards and enhance teamwork and communication. These developments may lead to increased productivity, maybe better audit quality, and more effective processes. But there are still difficulties. Data quality is essential to the success of AI integration, and auditors may exhibit bias in favor of human professionals. Careful thought must also be given to ethical issues about transparency and possible job displacement. All things considered, this study adds significant knowledge to both the academic literature and real-world auditing applications. It improves our comprehension of the function of AI in the industry and provides useful information for people involved in information technology development, audit, and legislation. © 2024 IEEE.

关键词： Data quality

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