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PADI-web for Plant Health Surveillance 36th

PADI-web for Plant Health Surveillance

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36th International Conference on Advanced information Systems Engineering (CAiSE)

作者： Roche, Mathieu Rabatel, Julien Trevennec, Carlene Pieretti, Isabelle CIRAD F-34398 Montpellier France INRAE F-34398 Montpellier France Univ Montpellier INRAE CNRS TETISCIRADAgroParisTech Montpellier France Univ Montpellier Inst Agro INRAE CIRADIRDPHIM Montpellier France Univ Montpellier INRAE CIRAD ASTRE Montpellier France Plateforme Natl Epidemiosurveillance Sante Anim Montpellier France Plateforme Natl Epidemiosurveillance Sante Vegeta Montpellier France

ISBN: (纸本)9783031609992;9783031610004

Due to the increasing number of new and reemerging pests resulting from intensification, globalisation and climate change, monitoring of plant health is crucial. In this context, outbreak detection in digital media could be useful for improving plant disease surveillance. But manually extracting relevant information from unofficial sources is time-consuming. The Platform for Automated extraction of Disease information from the web (PADI-web) has been developed initially for animal health surveillance, and recently for plant disease surveillance. In order to identify relevant news and information with this new PADI-web instance dedicated to plant health, machine learning approaches and language models (RoBERTa) have been integrated for monitoring plant diseases. This paper presents the PADI-web algorithms and visualisations implemented for specific case studies (i.e. Xylella fastidiosa and Fusarium Oxysporum Tropical) using text-mining approaches tuned on the plant disease domain.

关键词： Epidemic intelligence Event-based surveillance Web monitoring Text mining Plant health

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Towards a Hybrid Intelligence Paradigm: Systematic Integration of Human and Artificial Capabilities 18th

Towards a Hybrid Intelligence Paradigm: Systematic Integrati...

引用

18th International Conference on Research Challenges in information Sciences (RCIS)

作者： Mestre, Antoni Univ Politecn Valencia VRAIN Inst Camino Vera S-N Valencia 46022 Spain ValgrAI Valencian Grad Sch & Res Network Artifici Valencia Spain

ISBN: (纸本)9783031594670;9783031594687

The evolution of Artificial Intelligence from traditional inference-based systems to sophisticated generative models has blurred the boundaries between machine and human capabilities, giving rise to Hybrid Intelligence (HI). HI represents a symbiotic relationship between human and artificial intelligence, integrating human wisdom and expertise with machine intelligence. This work aspires to explore the paradigm shift towards HI, with a focus on integrating human expertise with machine intelligence. It aims to address challenges in human-machine interaction and dynamic task management within HI systems, emphasizing the necessity for seamless integration to fully exploit the capabilities of both entities. Through interdisciplinary collaboration and empirical inquiry, this research endeavors to advance understanding and implementation of HI systems across diverse domains, paving the way for systems that harness the intelligence of humans and machines to tackle complex challenges.

关键词： Hybrid intelligence human-machine interaction human-centered artificial intelligence dynamic task management

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Performance of Holistic Evaluation for Multi-criteria Decisions Comparing Selection or Elimination of Alternatives 10th

Performance of Holistic Evaluation for Multi-criteria Decisi...

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10th International Conference on Decision Support Systems Technologies (ICDSST)

作者： Soares de Vasconcelos, Tarsila Rani Peixoto Roselli, Lucia Reis de Almeida, Adiel Teixeira Univ Fed Pernambuco Ctr Decis Syst & Informat Dev CDSID Recife PE Brazil

ISBN: (纸本)9783031593758;9783031593765

In some decision situations, the use of Multi-Criteria DecisionMaking/Aiding methods cannot capture the real cognitive process performed by Decision-Makers. In this context, behavioral studies can be performed to investigate Decision-Makers' behavior during the decision process. This study has been undertaken to compare previous studies in order to investigate how Decision-Makers performed holistic evaluations using graphical and tabular visualization presented in the FITradeoff Decision Support System. Results may be applied to other methods using additive aggregation in the MAVT (Multi-Attribute Value Theory) context. The study aims to modify or transform the method based on the inclusion of behavioral aspects observed during the decision process. Thus, in this study an experiment has been designed to conduct holistic evaluations. Eight visualizations are presented arranged in bar graphs and tables, and two decision processes have been investigated - the selection of the best alternative and the elimination of the worst alternative. The experiment has been applied to 134 participants, and the data were collected and analyzed in terms of their probabilities of success and preferences.

关键词： Multi-Criteria Decision Making/Aiding (MCDM/A) Holistic evaluation Behavioral experiment FITradeoff Method

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Transfer Learning for Potato Leaf Disease Detection 18th

Transfer Learning for Potato Leaf Disease Detection

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18th International Conference on Research Challenges in information Sciences (RCIS)

作者： Ganie, Shahid Mohammad Hemachandran, K. Rege, Manjeet Woxsen Univ Dept Analyt AI Res Ctr Hyderabad 502345 India Univ St Thomas Dept Software Engn & Data Sci St Paul MN 55105 USA

ISBN: (纸本)9783031594670;9783031594687

Deep learning techniques have demonstrated significant potential in the agriculture sector to increase productivity, sustainability, and efficacy for farming practices. Potato is one of the world's primary staple foods, ranking as the fourth most consumed globally. Detecting potato leaf diseases in their early stages poses a challenge due to the diversity among crop species, variations in symptoms of crop diseases, and the influence of environmental factors. In this study, we implemented five transfer learning models including VGG16, Xception, DenseNet201, EfficientNetB0, and MobileNetV2 for a 3-class potato leaf classification and detection using a publicly available potato leaf disease dataset. Image preprocessing, data augmentation, and hyperparameter tuning are employed to improve the efficacy of the proposed model. The experimental evaluation shows that VGG16 gives the highest accuracy of 94.67%, precision of 95.00%, recall of 94.67%, and F1 Score of 94.66%. Our proposed novel model produced better results in comparison to similar studies and can be used in the agriculture industry for better decision-making for early detection and prediction of plant leaf diseases.

关键词： Plant leaf disease detection transfer learning image processing VGG16 Xception DenseNet201 EfficientNetB0 MobileNetV2

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ACTIVE SMOTE for Imbalanced Medical Data Classification 6th

ACTIVE SMOTE for Imbalanced Medical Data Classification

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6th International Conference on information and Knowledge Systems (ICIKS)

作者： Sena, Raul Ben Hamida, Sana Paris Dauphine Univ PSL Res Univ CNRS UMR 7243LAMSADE Paris France

ISBN: (纸本)9783031516634;9783031516641

Classifying imbalanced data is a big challenge for machine learning techniques, especially for medical data. To deal with this challenge, many solutions have been proposed. The most famous methods are based on the Synthetic Minority Over-sampling Technique (SMOTE), which creates new synthetic instances in the minority class. In this paper, we study the efficiency of the SMOTE-based methods on some imbalanced data sets. We then propose extending these techniques with Active Learning to control the evolution of the minority class better. Active Learning uses uncertainty and diversity sampling to choose wisely the data points from which the synthetic samples will be generated. To evaluate our approach, we make comprehensive experimental studies on two medical data sets for diabetes diagnosis and breast cancer diagnosis.

关键词： Imbalanced medical data Machine Learning SMOTE Active Learning Diversity Sampling Uncertainty Sampling Diabetes Diagnosis Breast Cancer Detection

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Examining the Effect of ChatGPT on Small Group Ideation Discussions 24th

Examining the Effect of ChatGPT on Small Group Ideation Disc...

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24th International Conference on Group Decision and Negotiation (GDN)

作者： Chosokabe, Madoka Koie, Shohei Oyamada, Yuji Tottori Univ 4-101 Koyama Minami Tottori Japan

ISBN: (纸本)9783031593727;9783031593734

In discussions among residents, such as those in town planning workshops, participants often engage in conversations and propose ideas for community development based on given themes. However, due to differences in knowledge about the themes among participants, establishing a common understanding takes time, and there is a limited timeframe for considering and consolidating ideas. As a result, facilitators play a crucial role in guiding the discussions among residents, but there is a shortage of facilitators relative to the opportunities for discussions. Consequently, it is challenging for participants alone to consolidate high-quality ideas. Generative AI tools like ChatGPT are anticipated to be valuable in supporting idea generation. On the other hand, there is a risk of developing ideas overly dependent on generative AI if used improperly. Therefore, it is crucial to first understand the effect that generative AI can have on group discussions. In this paper, we conduct a user study to examine effective usages of ChatGPT during small group ideation discussions. From this experiment, we discovered and categorized both the positive and negative effects from utilizing ChatGPT.

关键词： Participatory Planning Process Small Group Discussion ChatGPT

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Using Unfolding Analysis and MARS Approach for Generating a Scoring System from a Group Preference information 23rd

Using Unfolding Analysis and MARS Approach for Generating a ...

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23rd International Conference on Group Decision and Negotiation (GDN)

作者： Wachowicz, Tomasz Roszkowska, Ewa Filipowicz-Chomko, Marzena Univ Econ Katowice 1 Maja 50 PL-40287 Katowice Poland Bialystok Tech Univ Wiejska 45A PL-15351 Bialystok Poland

ISBN: (纸本)9783031337796;9783031337802

This paper proposes a new approach for determining the negotiation offer scoring system for a negotiator out of the group preference information. The proposed algorithm, unMAGIC (unfolding and MARS-based Group preference information Computation), is a hybrid of the unfolding and MARS (Measuring Attractiveness near Reference Solution) methods. In its first steps, the experts express their preferences over some exemplary negotiation offers, predefined according to the principles of the MARS approach, by declaring their rank order. Then, the rank orders are processed according to the principles of unfolding analysis to produce two-dimensional evaluations of each offer that describe how much it is preferred from the aggregated viewpoint. Offers and experts are also depicted on the plane, showing the distances to each other, which helps the negotiator better understand the variety of recommendations. Then, using the principles of MARS, these distances are used to build the additive scoring system for the negotiator. Finally, we verify how our hybrid approach works using a dataset from the prenegotiation experiment based on a bilateral negotiation case.

关键词： Preference elicitation Negotiation scoring system Collective negotiation profile Unfolding method MARS method

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Execution Semantics for Process Choreographies with Data 21st

Execution Semantics for Process Choreographies with Data

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21st International Conference on business Process Management (BPM)

作者： Lichtenstein, Tom Weske, Mathias Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9783031416224;9783031416231

Interaction models, such as BPMN choreography diagrams, enable the coordination of interactions between organizations within a process choreography. Since choreography participants typically do not share a central data store, data flow must be considered during design to ensure that each participant has sufficient data to continue a conversation. However, current choreography modeling languages lack a concise notation and execution semantics for data exchange. This paper refines the data flow specifications of choreography diagrams using supplemental models. Furthermore, the execution semantics for data exchange is formally defined by data-enhanced interaction Petri nets. The extended semantics enable the analysis of data awareness and data consistency at design time.

关键词： Process choreographies Interaction Petri nets Data flow

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Combining Process Mining and Optimization: A Scheduling Application in Healthcare

Combining Process Mining and Optimization: A Scheduling Appl...

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20th International Conference on business Process Management (BPM) / business Process Management (BPM) Forum

作者： Di Cunzolo, Matteo Guastalla, Alberto Aringhieri, Roberto Sulis, Emilio Amantea, Ilaria Angela Ronzani, Massimiliano Di Francescomarino, Chiara Ghidini, Chiara Fonio, Paolo Grosso, Marco Univ Torino Dept Comp Sci Corso Svizzera 185 I-10149 Turin Italy Fdn Bruno Kessler Via Sommar 18 I-38123 Povo TN Italy Univ Torino Radiol Unit Dept Surg Sci Via Genova 3 I-10126 Turin Italy AOU City Hlth & Sci Dept Radiol Corso Bramante 88 I-10126 Turin Italy

ISBN: (纸本)9783031253829;9783031253836

Optimizing the scheduling of operating rooms is quite a challenging task, as different aspects, some of which the medical personnel is not completely aware of, may have a strong impact on the scheduling and need to be taken into account. This work aims at addressing such a problem by proposing a framework that combines process analysis and operations research. Process mining techniques are used for analysing interventional radiology data collected from the information system of a hospital and identifying delays and lagging cases, as well as the causes of these delays. Leveraging the knowledge acquired by looking at data (e.g., the procedures that are more often delayed), an optimization model able to take into account these aspects is designed. This paper describes the preliminary results of a proof-of-concept based on 3 months real-life data. The results show that, taking into account the information discovered from data, allows for obtaining a more accurate scheduling.

关键词： Scheduling Optimization Process mining Radiology

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Value Creation from Data Science Applications - A Literature Review 22nd

Value Creation from Data Science Applications - A Literature...

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22nd International Conference on Perspectives in business Informatics Research (BIR)

作者： Pohl, Matthias Haertel, Christian Turowski, Klaus Otto von Guericke Univ Fac Comp Sci Magdeburg Res & Competence Cluster VLBA Magdeburg Germany

ISBN: (纸本)9783031431258;9783031431265

In the paper at hand, a structured literature review was conducted to provide an overview of the components of value creation from Data Science applications. For this purpose, the value net as a concept of value creation was used as a framework. The value net for Data Science applications is intended to provide a helpful starting point for identifying the business value of Data Science applications in enterprises. In the future, the proposed value net is used for the classification of various projects and applications in the context of a case study. Further development as a tool for detecting value-adding potentials of Data Science in a business context is a future objective of this research.

关键词： Data Science Data Analytics value creation value creation network literature review

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