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检索条件"任意字段=20th Conference on System Modeling and Optimization"

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A Proposed Mala-Net CNN Algorithm for Malaria Early Detection Based on thick Blood Smear

A Proposed Mala-Net CNN Algorithm for Malaria Early Detectio...

引用

Information Technology and Digital Innovation (ISITDI), International Symposium on

作者： Zul Indra Yessi Jusman Elfizar Evfi Mahdiyah Roni Salambue Doni Winarso Departement of Computer Science Universitas Riau Pekanbaru Indonesia Departement of Electrical Engineering Universitas Muhammadiyah Yogyakarta Bantul Indonesia Departement of Information System Universitas Muhammadiyah Riau Pekanbaru Indonesia

ISBN: (数字)9798350389241

ISBN: (纸本)9798350389258

Malaria has spread worldwide since the early 20th century and causes nearly half a million deaths each year. Malaria is actually not a dangerous disease and can be easily cured if the treatment can be carried out effectively. therefore, efforts to identify the presence of the plasmodium parasite early will greatly help reduce the death rate caused by this disease. However, the fact is that this disease often does not get serious attention. Generally, this disease is only treated in the critical phase because it is considered just a common flu. this research is intended to produce a computer-aided disease diagnosis (CAD) system enriched with CNN algorithms to assist in early malaria diagnosis. this CAD system is expected to provide an effective and reliable malaria diagnosis, and avoid the limitations of manual diagnosis. In order to develop the CAD applications, this study proposed a novel CNN architecture called as Mala-Net by utilizing red thick blood dataset. In conclusion, this study successfully surpassed previous studies used as benchmarks with an accuracy value above 97%.

关键词： Training Technological innovation Solid modeling Accuracy Malaria Mortality Classification algorithms Reliability Blood optimization

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Biometric Recognition in a Multi-Sample Multi-Subject Facial Image Database: the 1:M:N system Model 20

Biometric Recognition in a Multi-Sample Multi-Subject Facial...

引用

20th Annual International conference of the Biometrics-Special-Interest-Group (BIOSIG )

作者： Halfen, DeWayne Rajaraman, Srinivasan Wayman, James L. Booz Allen Hamilton Arlington VA 22203 USA Booz Allen Hamilton Laurel MD USA Off Biometr Identificat Management Dept Homeland Secur Arlington VA USA

ISBN: (纸本)9783885797098

Over the last 50 years, biometric recognition has advanced from localized "identity verification" applications [1] [2] to include large-scale systems in which "a determination is made as to the identity of an individual independently of any information supplied by the individual" [1]. Models for estimating and expressing system error rates (both false matches and false non-matches) have been largely limited to so-called "1-to-1" and "1-to-N" systems in which each identity is represented by only one enrolled reference [3]. In this paper, we create a highly simplified simulation model for a common current situation in which each known identity record has multiple stored references. We call this the "1:M:N" model and show that both DET and CMC performance depend upon the number of identities and references per identity, not simply the total number of reference images, as usually assumed. Although trialed here on very simple decision policies, this model will be extended in future work to more complex decision criteria.

关键词： biometric system modeling large-scale systems Detection Error Tradeoff curve cumulative match characteristics

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Steel Surface Defect Detection Based on Improved YOLOv8

Steel Surface Defect Detection Based on Improved YOLOv8

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International conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Jichen Zhang Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Hubei China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

there are some defects on the surface of steel that are difficult to detect, so we propose an improved algorithm based on YOLOv8 for detecting defects that are difficult to be detected. this improvement includes the introduction of an SPD module to minimize the problem of information loss due to downsampling; the addition of a small object detection head combined with EMA attention to further enhance the detection capabilities for small targets; the use of BiFPN to replace the original PANet, applying different weights to different features to strengthen feature fusion. Experiments were conducted on the NEU-DET dataset, and the algorithm improved mAP to 80.2%, an improvement of 3.9% over the baseline model.

关键词： Convolution Computational modeling Object detection Feature extraction Knowledge discovery Steel Data mining Background noise Defect detection Fuzzy systems

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Machine Learning-Based Intelligent system for Heart Disease Prediction

Machine Learning-Based Intelligent System for Heart Disease ...

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International conference on Computer and Communications Technologies (ICCCT)

作者： S. Shonika R. Raguram I. Siva Sundar Rajan Rajarajan. TM Dept of Information Technology Sri Sairam Institute of Technology Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Late in the 20th century, heart disease began to assume the stature of the leading cause of death in the world. Hence, finding ways to notice heart diseases early on is an important step in lowering the risk factors involved and ensuring better outcomes for patients. the current paper presents a model based on machine learning for predicting heart diseases by applying Logistic Regression, Naive Bayes, and XGBoost. Various medical datasets, which include demographic patient information, clinical measurements, and historical health data, have undergone preprocessing with a series of techniques ranging from the filling in of missing data, normalization, to categorical encoding, thus ensuring the quality of the respective data. Binary classification is achieved with Logistic Regression and Naive Bayes, while XGBoost improves model accuracy and minimizes overfitting. Model performance is evaluated based on accuracy, precision, recall, Fl score, and cross-validation for reliability. this paper thus highlights the potential of machine learning technology in assisting early detection and clinical decision-making in healthcare through the use of these advanced techniques.

关键词： Heart Logistic regression Accuracy Computational modeling Predictive models Probabilistic logic Chatbots Data models Bayes methods Diseases

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Rural Handover Parameter Tuning to Achieve End to End Latency Requirements of Future Railway Mobile Communication systems

Rural Handover Parameter Tuning to Achieve End to End Latenc...

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IEEE International conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Dogukan Atik Murat Gursu Fidan Mehmeti Wolfgang Kellerer Nokia Standards Nokia Solutions and Networks GmbH Munich Germany Chair of Communication Networks Technical University of Munich Munich Germany

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

GSM-R (GSM for Railways) is a 2 $G$ -based standardized ground-to-train communications system that enabled interoperability across different countries. However, as a 2G-based system, it is nearing its lifetime and therefore, it will be replaced with 5G-based Future Railway Mobile Communications system (FRMCS). FRMCS is expected to bring in new use cases that demand low latency and high reliability. However, from a mobility perspective, it is not clear how the low latency and high reliability will be achieved. this paper investigates the effect of handover procedure on latency and reliability and analyzes which use cases of FRMCS can be satisfied using baseline handover. We also sweep through different handover parameter configurations and analyze their effect on mobility performance. then, we analyze the effect of mobility performance on packet latency and reliability. Our results show that, with baseline handover, Standard Data Communications Scenario is met and optimizing for baseline handover performance can reduce latency by up to 18.5%, indicating that optimizing for mobility performance is crucial in FRMCS.

关键词： Handover Mobile communication Reliability engineering Rail transportation Reliability Low latency communication Standards Tuning optimization Mobile computing

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Tackling Environmental Variability: Few Shot Segmentation for Domain-Adaptive Weed Segmentation in Agricultural Robotics

Tackling Environmental Variability: Few Shot Segmentation fo...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Nico Catalano Monica Leone Matteo Matteucci Department of Electronics Information and Bioengineering Politecnico di Milano

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

this paper investigates the application of Few Shot Segmentation techniques in addressing weed management challenges within agricultural robotics. Traditional methods, such as indiscriminate herbicide spraying, pose environmental and economic concerns, emphasize the necessity of developing automated precision procedures. Semantic Segmentation models can offer significant advancements in weed management within agricultural robotics but can be negatively impacted by domain shifts, often requiring adaptation, hindering the development of the field. In this context, we propose a Few Shot Segmentation-based approach for precise weed segmentation in corn and bean cultivations. Our model, adapted from PFENet, exhibits robust performance across varying environmental conditions, mitigating domain shift issues. Evaluation on the ROSE dataset demonstrates the model’s reliability and generalizability, highlighting the potential of Few Shot Segmentation in advancing agricultural robotics technologies.

关键词： Economics Adaptation models Computer aided software engineering Automation Biological system modeling Semantic segmentation Spraying Predictive models Reliability Robots

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A Rank Based ACO Approach for Optimal Resource Allocation and Scheduling in FMS Modeled with Labelled Petri Net 20

A Rank Based ACO Approach for Optimal Resource Allocation an...

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20th International conference on New Trends in Intelligent Software Methodologies, Tools and Techniques (SoMeT)

作者： Jablaoui, Amira Kmimech, Hichem Sliman, Layth Nabli, Lotfi Univ Monastir Higher Inst Comp Sci & Math Monastir Tunisia Univ Monastir Res Lab Automat Signal & Image Proc Monastir Tunisia EFREI Paris Villejuif France Univ Paris Saclay IBISC F-91020 Evry Courcouronnes France

ISBN: (纸本)9781643681955;9781643681948

In this article, we study the NP-Hard combinatorial optimization problem of the minimum initial marking (MIM) computation in labeled Petri net (L-PN) while considering a sequence of labels to minimize the resource consumption in a flexible manufacturing system (FMS), and we propose an approach based on the ant colony optimization (ACO) precisely the extension Rank-based ACO to optimal resource allocation and scheduling in FMS. the ACO meta-heuristic is inspired by the behavior of ants in foraging based on pheromones deposit. the numerical results show that the proposed algorithm obtained much better results than previous studies.

关键词： Labeled Petri Nets Flexible Manufacturing system Minimum Initial Marking Scheduling Resource Allocation Label sequence Ant Colony optimization

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Constrained Clustering Problems: New optimization Algorithms 20th

Constrained Clustering Problems: New Optimization Algorithms

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20th International conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Ibn-Khedher, Hatem Hadji, Makhlouf Ibn Khedher, Mohamed Khebbache, Selma ALTRAN Labs F-78140 Velizy Villacoublay France Inst Rech Technol SystemX 8 Ave Vauve F-91120 Palaiseau France

ISBN: (纸本)9783030878979;9783030878962

Constrained clustering problems are often considered in massive data clustering and analysis. they are used in modeling various issues in anomaly detection, classification, systems' misbehaviour, etc. In this paper, we focus on generalizing the K-Means clustering approach when involving linear constraints on the clusters' size. Indeed, to avoid local optimum clustering solutions which consists in empty clusters or clusters with few points, we propose linear integer programming approaches based on relaxation and rounding techniques to cope with scalability issues. We show the efficiency of the new proposed approach, and assess its performance using five data-sets from different domains.

关键词： Constrained-clustering K-Means Combinatorial optimization

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Develop a New Method for Water Ecological Health Evaluation Incorporating Machine Learning

Develop a New Method for Water Ecological Health Evaluation ...

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International conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Wenyu Zhang Qi Ding Yifei Wang Rui Li School of Economics and Management Xi'an University of Posts and Telecommunications Xi'an China School of Modern Post Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

With the rapid development of China's society and economy, water ecological problems are becoming more and more prominent. this study proposes a new evaluation method of water ecological health. First, an evaluation index system based on the DPSIR model is constructed; second, a new water ecological health assessment model combining machine learning is constructed by integrating objective and subjective weights through game theory ideas, and the water ecological health status of Shaanxi Province in the past five years is comprehensively evaluated; finally, the temporal and space change characteristics of the watersheds in Shaanxi are deeply analyzed to reflect the water ecological health status of Shaanxi Province, and the interrelationship and influence between human activities and natural system, analyzing the interrelationships and impacts between human activities and natural systems, and revealing the urgency and criticality of water ecology management in Shaanxi Province. the study concludes with recommendations to ensure the long-term sustainable development of Shaanxi's water ecology, which will provide a scientific basis for future water resource management.

关键词： Water Analytical models Biological system modeling Machine learning Watersheds Water quality Ecology Sustainable development Socioeconomics Water resources

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Analytical Method to Determine the Test Positions for Validation of a Two-Dimensional Shuttle system Model 20

Analytical Method to Determine the Test Positions for Valida...

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20th International conference on modeling and Applied Simulation, MAS 2021

作者： Siciliano, Giulia Schuster, Christian U. Fottner, Johannes Material Flow Logistics Technical University of Munich Boltzmannstraße 15 Garching bei München 85748 Germany Department of Physics Technical University of Munich James-Franck-Straße 1 Garching bei München 85748 Germany

ISBN: (纸本)9788885741614

Test positions are necessary to efficiently validate that the simulation and physical implementation of a warehouse are in agreement. Test positions denote the retrieval and storage positions that give values representative for the average performance. In this paper, we develop an analytical method to determine these test positions for the shuttle base tier of a highly-dynamic, hybrid pallet storage and retrieval system or dynamic hybrid pallet warehouse (DHPW). the analytical method developed is also valid for any other shuttle system, regardless of the layout or the load carriers. Moreover, the analytical method developed does not require the hypothesis of uniform filling of the warehouse. In this paper, first, we briefly describe the system under consideration. Next, we review the relevant existing methods to determine test positions for shuttle-based warehouses. Afterwards, we develop our concept for the analytical method to determine the test positions. In conclusion, we verify the concept developed through discrete event simulation experiments. © 2021 the Authors.

关键词： Discrete event simulation

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