KI - Künstliche Intelligenz最新文献_第5页

EpiPredict: Agent-Based Modeling of Infectious Diseases EpiPredict：基于代理的传染病建模

KI - Künstliche Intelligenz Pub Date : 2023-11-15 DOI: 10.1007/s13218-023-00819-4

Janik Suer, Johannes Ponge, Bernd Hellingrath

引用次数: 0

Data Augmentation for Mask-Based Leaf Segmentation of UAV-Images as a Basis to Extract Leaf-Based Phenotyping Parameters 基于掩模的无人机图像叶片分割数据增强提取叶片表型参数

KI - Künstliche Intelligenz Pub Date : 2023-11-14 DOI: 10.1007/s13218-023-00815-8

Abel Barreto, Lasse Reifenrath, Richard Vogg, Fabian Sinz, Anne-Katrin Mahlein

{"title":"Data Augmentation for Mask-Based Leaf Segmentation of UAV-Images as a Basis to Extract Leaf-Based Phenotyping Parameters","authors":"Abel Barreto, Lasse Reifenrath, Richard Vogg, Fabian Sinz, Anne-Katrin Mahlein","doi":"10.1007/s13218-023-00815-8","DOIUrl":"https://doi.org/10.1007/s13218-023-00815-8","url":null,"abstract":"Abstract In crop protection, disease quantification parameters such as disease incidence (DI) and disease severity (DS) are the principal indicators for decision making, aimed at ensuring the safety and productivity of crop yield. The quantification is standardized with leaf organs, defined as individual scoring units. This study focuses on identifying and segmenting individual leaves in agricultural fields using unmanned aerial vehicle (UAV), multispectral imagery of sugar beet fields, and deep instance segmentation networks (Mask R-CNN). Five strategies for achieving network robustness with limited labeled images are tested and compared, employing simple and copy-paste image augmentation techniques. The study also evaluates the impact of environmental conditions on network performance. Metrics of performance show that multispectral UAV images recorded under sunny conditions lead to a performance drop. Focusing on the practical application, we employ Mask R-CNN models in an image-processing pipeline to calculate leaf-based parameters including DS and DI. The pipeline was applied in time-series in an experimental trial with five varieties and two fungicide strategies to illustrate epidemiological development. Disease severity calculated with the model with highest Average Precision (AP) shows the strongest correlation with the same parameter assessed by experts. The time-series development of disease severity and disease incidence demonstrates the advantages of multispectral UAV-imagery in contrasting varieties for resistance, as well as the limits for disease control measurements. This study identifies key components for automatic leaf segmentation of diseased plants using UAV imagery, such as illumination and disease condition. It also provides a tool for delivering leaf-based parameters relevant to optimize crop production through automated disease quantification by imaging tools.","PeriodicalId":192555,"journal":{"name":"KI - Künstliche Intelligenz","volume":"32 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134954469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing Semantically Enriched Trajectories 分析语义丰富的轨迹

KI - Künstliche Intelligenz Pub Date : 2023-11-14 DOI: 10.1007/s13218-023-00818-5

Jana Seep

{"title":"Analyzing Semantically Enriched Trajectories","authors":"Jana Seep","doi":"10.1007/s13218-023-00818-5","DOIUrl":"https://doi.org/10.1007/s13218-023-00818-5","url":null,"abstract":"Abstract In order to understand what influences the movement of an object or person it is important to consider a variety of factors. These could be the visibility of certain landmarks, the current temperature or the presence of a crowded area to be avoided. These insights then can be used to understand movement in the public sector and improve our build environment, e.g. to reduce street traffic accidents or orientation in complex buildings. The following extended abstract is a summary of a doctoral thesis submitted to the University of Münster. The thesis was successfully defended in February 2023 [16]. The dissertation focuses on the analysis of so-called semantically enriched trajectories , which are used to describe observed movement. It proposes a new model based on an extended finite state machine, which allows for the representation and consideration of the information about the context of the trajectory. With the new model, we consider two main steps in trajectory analysis: First, we aim to infer a semantically enriched representative trajectory for a given cluster of trajectories. Second, we introduce a variation of the well-known k-means algorithm to calculate clusters based on the given context of trajectories. To show semantic feasibility of our approach, we conclude this work by evaluating the possibility to provide decision support for domain experts in two different public sector related contexts.","PeriodicalId":192555,"journal":{"name":"KI - Künstliche Intelligenz","volume":"32 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How to design a value-based Chatbot for the manufacturing industry: An empirical study of an internal assistance for employees 如何为制造业设计一个基于价值的聊天机器人:对员工内部协助的实证研究

KI - Künstliche Intelligenz Pub Date : 2023-11-13 DOI: 10.1007/s13218-023-00817-6

Patrick Berger, Joerg von Garrel

引用次数: 0

Dissertation Abstract: Taming Exact Inference in Temporal Probabilistic Relational Models 论文摘要:时间概率关系模型中的精确推理

KI - Künstliche Intelligenz Pub Date : 2023-11-10 DOI: 10.1007/s13218-023-00813-w

Marcel Gehrke

引用次数: 0

Grassland Yield Estimation Using Transfer Learning from Remote Sensing Data 基于遥感数据迁移学习的草地产量估算

KI - Künstliche Intelligenz Pub Date : 2023-11-07 DOI: 10.1007/s13218-023-00814-9

Elias Eder, Peter Riegler-Nurscher, Johann Prankl, Heinrich Prankl

引用次数: 0

Towards noise robust acoustic insect detection: from the lab to the greenhouse 面向噪声鲁棒昆虫声学检测:从实验室到温室

KI - Künstliche Intelligenz Pub Date : 2023-10-30 DOI: 10.1007/s13218-023-00812-x

Jelto Branding, Dieter von Hörsten, Jens Karl Wegener, Elias Böckmann, Eberhard Hartung

{"title":"Towards noise robust acoustic insect detection: from the lab to the greenhouse","authors":"Jelto Branding, Dieter von Hörsten, Jens Karl Wegener, Elias Böckmann, Eberhard Hartung","doi":"10.1007/s13218-023-00812-x","DOIUrl":"https://doi.org/10.1007/s13218-023-00812-x","url":null,"abstract":"Abstract Successful and efficient pest management is key to sustainable horticultural food production. While greenhouses already allow digital monitoring and control of their climate parameters, a lack of digital pest sensors hinders the advent of digital pest management systems. To close the control loop, digital systems need to be enabled to directly assess the state of different insect populations in a greenhouse. The presented article investigates the feasibility of acoustic sensors for insect detection in greenhouses. The study is based on an extensive dataset of acoustic insect recordings made with an array of high-quality microphones under noise-shielded conditions. By mixing these noise-free laboratory recordings with environmental sounds recorded with the same equipment in a greenhouse, different signal-to-noise ratios (SNR) are simulated. To explore the possibilities of this unique and novel dataset, two deep-learning models are trained on this simulation data. A simple spectrogram-based model represents the baseline for a comparison with a model capable of processing multi-channel raw audio data. Making use of the unique possibility of the dataset, the models are pre-trained on clean data and fine-tuned on noisy data. Under lab conditions, results show that both models can make use of not just insect flight sounds but also the much quieter sounds of insect movements. First attempts under simulated real-world conditions showed the challenging nature of this task and the potential of spatial filtering. The analysis enabled by the proposed methods for training and evaluation provided valuable insights that should be considered for future work.","PeriodicalId":192555,"journal":{"name":"KI - Künstliche Intelligenz","volume":"121 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Opportunities and Challenges for AI-Based Analysis of RWD in Pharmaceutical R&D: A Practical Perspective 基于人工智能的制药研发RWD分析的机遇与挑战:一个实践视角

KI - Künstliche Intelligenz Pub Date : 2023-10-09 DOI: 10.1007/s13218-023-00809-6

Merle Behr, Rolf Burghaus, Christian Diedrich, Jörg Lippert

{"title":"Opportunities and Challenges for AI-Based Analysis of RWD in Pharmaceutical R&D: A Practical Perspective","authors":"Merle Behr, Rolf Burghaus, Christian Diedrich, Jörg Lippert","doi":"10.1007/s13218-023-00809-6","DOIUrl":"https://doi.org/10.1007/s13218-023-00809-6","url":null,"abstract":"Abstract Real world data (RWD) has become an important tool in pharmaceutical research and development. Generated every time patients interact with the healthcare system when diagnoses are developed and medical interventions are selected, RWD are massive and in many regards typical big data. The use of artificial intelligence (AI) to analyze RWD seems an obvious choice. It promises new insights into medical need, drivers of diseases, and new opportunities for pharmacological interventions. When put into practice RWD analyses are challenging. The distributed generation of data, under sub-optimally standardized conditions in a patient-oriented but not information maximizing healthcare transaction, leads to a high level of sparseness and uncontrolled biases. We discuss why this needs to be addressed independent of the type of analysis approach. While classical statistical analysis and modeling approaches provide a rigorous framework for the handling of bias and sparseness, AI methods are not necessarily suited when applied naively. Special precautions need to be taken from choice of method until interpretation of results to prevent potentially harmful fallacies. The conscious use of prior medical subject matter expertise may also be required. Based on typical application examples we illustrate challenges and methodological considerations.","PeriodicalId":192555,"journal":{"name":"KI - Künstliche Intelligenz","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Novel Deep Learning Approaches for Analyzing Diffusion Imaging Data 扩散成像数据分析的新型深度学习方法

KI - Künstliche Intelligenz Pub Date : 2023-09-28 DOI: 10.1007/s13218-023-00811-y

Yousef Sadegheih, Leon Weninger, Dorit Merhof

{"title":"Novel Deep Learning Approaches for Analyzing Diffusion Imaging Data","authors":"Yousef Sadegheih, Leon Weninger, Dorit Merhof","doi":"10.1007/s13218-023-00811-y","DOIUrl":"https://doi.org/10.1007/s13218-023-00811-y","url":null,"abstract":"Abstract Diffusion magnetic resonance imaging (dMRI) is developing into one of the most important non-invasive tools for clinical brain research. This development is supported by a project funded by the German Research Foundation, in which four major obstacles related to dMRI data were addressed: (1) the lack of transferability of dMRI data between clinical sites, (2) the lack of training and label data, (3) the potential of complex diffusion data, and (4) the integration of spherical signals in neural networks to improve accuracy. To overcome the problem of different MRI systems producing slightly varying data, the project developed a method for harmonizing MRI signals. To address the issue of limited ground truth data, a framework was developed to synthesize individual diffusion data and complete datasets based on important diffusion characteristics and statistics. The integration of complex signals, often discarded during acquisition, to improve reconstruction was also explored. Finally, new methods were developed to preserve the spherical character of the diffusion data in the DL model. The resulting methods are intended to improve the usability of diffusion imaging data and to enable the creation of processing pipelines for dMRI data in clinical studies and clinical practice.","PeriodicalId":192555,"journal":{"name":"KI - Künstliche Intelligenz","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135387363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Designing Expert-Augmented Clinical Decision Support Systems to Predict Mortality Risk in ICUs 设计专家增强临床决策支持系统以预测icu的死亡风险

KI - Künstliche Intelligenz Pub Date : 2023-08-19 DOI: 10.1007/s13218-023-00808-7

Johannes Chen, Maximilian Lowin, Domenic Kellner, O. Hinz, E. Adam, Angelo Ippolito, Katharina Wenger-Alakmeh

引用次数: 0