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共 66980 条记录，以下是201-210 订阅

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Federated Learning for Enhanced Cell Nuclei Segmentation in Histopathological Images

Federated Learning for Enhanced Cell Nuclei Segmentation in ...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Usai, Marco Loddo, Andrea Putzu, Lorenzo Ruberto, Cecilia Di University of Cagliari Dept. of Mathematics and Computer Science Cagliari Italy University of Cagliari Dept. of Electrical and Electronic Engineering Cagliari Italy

ISBN: (纸本)9798350362480

This study addresses the critical challenge of cell nuclei segmentation in histopathological image analysis, which is essential for cancer diagnosis and prognosis. Traditional segmentation methods often struggle with complexities such as overlapping nuclei and variations in shape and size. Recent advancements in machine learning and deep learning, particularly convolutional neural networks, have improved segmentation accuracy but face limitations due to the need for large annotated datasets, often constrained by privacy regulations in the medical field. This research explores federated learning as a solution, enabling collaborative model training across institutions without sharing sensitive patient data. By leveraging diverse datasets while maintaining privacy, federated learning enhances model robustness and generalization capabilities. The study evaluates various federated learning techniques on in- and out-of-domain test sets, aiming to improve the reliability of segmentation models in clinical settings. The findings suggest that federated learning techniques can effectively address the challenges of data scarcity and domain shift, paving the way for more accurate and widely applicable segmentation algorithms in computational pathology. © 2024 IEEE.

关键词： Federated learning

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Optimizing Multi-Class Classification in Educational Data with Ensemble Learning and Data Balancing Techniques 5

Optimizing Multi-Class Classification in Educational Data wi...

引用

5th International Conference on Intelligent Data science Technologies and Applications, IDSTA 2024

作者： Al-Hammouri, Mohammad F. Hammouri, Ziad Akram Ali Almalkawi, Islam T. Lafee, Ansam The Hashemite University Dept. of Computer Engineering Zarqa Jordan Middle East University Dept. of Computer Science Amman Jordan

ISBN: (纸本)9798350354751

The field of education is increasingly embracing AI tools to improve student outcomes. This work aims to reduce academic failure in higher education by employing machine learning techniques to identify at-risk students early in their educational journey, enabling the implementation of supportive strategies to assist them. This study examines a dataset from a higher education institution and utilizes it to develop a classification model for predicting students' academic performance. The problem is formulated as a multi-class classification task with three categories: Graduate, Enrolled, and Dropout, with a significant imbalance skewed toward the Graduate. To improve prediction accuracy toward the minority class, the data balancing technique SMOTE with Edited Nearest Neighbor (SMOTE-ENN) is applied. Three popular classification models - Random Forest, XGBOOST and CatBoost - are employed. The findings show that SMOTE-ENN significantly improves classification results. Moreover, XGBOOST demonstrated the highest accuracy (94.6%) in correctly identifying all classes, as evidenced by the confusion matrix evaluation, achieving the highest results compared to previous work in the literature. Implementing these models allows for accurate predictions of students' performance and helps reduce dropout rates. © 2024 IEEE.

关键词： Contrastive Learning

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Using the Wavelet Packet Transform to Evaluate Parameters of Harmonics Clustered in Quadruples Using Linear Systems

Using the Wavelet Packet Transform to Evaluate Parameters of...

引用

2024 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2024

作者： Nicolae, Ileana-Diana Nicolae, Petre - Marian Nicolae, Marian-Ştefan University of Craiova Dept. of Computer Science and It Craiova Romania University of Craiova Dept. of Electrical Engineering Energetic and Aeronautics Craiova Romania

ISBN: (纸本)9798350360394

The paper presents an original technique used to evaluate the parameters of quadruples of harmonics clustered in an almost exclusive manner to quadruples of nodes from the bottom level of a Wavelet Packet Tree. The harmonic signal (RHS) generated by harmonics from a quadruple can be deduced with approximation from the analyzed signal by using original computational techniques. Linear systems were conceived based on the harmonics superposition and randomly chosen components of RHC (the approximated version of RHS). Their solving yields sets of possible solutions, that are afterward refined to exclude values with non-zero imaginary parts or deviant values. Averages over refined values are used. The indetermination relative to phase-shifts is solved considering the criterion of "best approximation"of RHC by the signal obtained through the superposition of the computed clustered harmonics. The harmonics from the studied quadruples are: (11, 13, 19, 21), (23,25,39,41) and (27,29,35,37). This paper provides the complementary technique for the odd harmonic identification within the range 3...41 (the odd harmonics not belonging to quadruples are coupled in pairs and were approached in a previous publication). © 2024 IEEE.

关键词： Wavelet analysis

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MAD-TI: Meta-Path Aggregated-Graph Attention Network for Drug Target Interaction Prediction 31

MAD-TI: Meta-Path Aggregated-Graph Attention Network for Dru...

引用

31st International Conference on electrical engineering, ICEE 2023

作者： Shami Tanha, Reza Sadighian, Maryam Zabihian, Arash Hooshmand, Mohsen Afsharchi, Mohsen Dept. Computer Science and It Zanjan Iran Tehran Iran University of Zanjan Dept. Electrical and Computer Engineering Zanjan Iran

ISBN: (纸本)9798350312560

Computational identification of unknown drug-target interactions (DTI) is crucial in locating new drug treat-ments for proteins, viruses, and diseases. This work proposes MAD-TI a meta-path-based, GAT-oriented method to predict DTIs. Our proposed method uses a heterogeneous graph of drugs, targets, diseases, and side effects as the input graph. Then, it applies two graph attention networks to generate the embeddings of drugs and targets. Using the embeddings, it predicts the unknown DTIs. The results show that MAD-TI outperforms the state-of-the-art methods. © 2023 IEEE.

关键词： Embeddings

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qCrop: An IoT Based Framework to Enhance Crop Productivity in Smart Agriculture 32

qCrop: An IoT Based Framework to Enhance Crop Productivity i...

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32nd IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Shamsa, Mahdi Rachakonda, Laavanya Mohanty, Saraju P. Kougianos, Elias University of North Texas Dept. of Computer Science and Engineering Denton United States University of North Carolina Wilmington Dept. of Computer Science Wilmington United States University of North Texas Dept. of Electrical Engineering Denton United States

ISBN: (纸本)9798331539672

Food is crucial in our life. Despite food manufacturers' efforts to meet consumers' needs with manufactured food, it cannot attain the identical level of quality and flavor as natural food. Some fruits and vegetables are classified as food that cannot be manufactured. Also, they play an essential role worldwide. This paper focuses on how farmers can protect their farms from an object that might threaten the crops and cause damage to them. It proposes a framework that farmers may utilize to safeguard the crops against any species of birds. Considering the adverse impact of bird attacks on crop production, this system effectively addresses this problem and consistently enhances the quality and quantity of them. It utilizes computer vision technology to establish a secure environment for the crop. This system is called Quality of Crop Device (qCrop), and it works with a You Only Look Once (Yolov8m) model to detect birds with high accuracy to protect farms. © 2024 IEEE.

关键词： Agri-food

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Self-supervised Registration and Segmentation of the Ossicles with A Single Ground Truth Label

Self-supervised Registration and Segmentation of the Ossicle...

引用

Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Zhang, Yike Noble, Jack H. Dept. of Computer Science Vanderbilt University United States Dept. of Electrical and Computer Engineering Vanderbilt University United States

ISBN: (纸本)9781510660373

AI-assisted surgeries have drawn the attention of the medical image research community due to their real-world impact on improving surgery success rates. For image-guided surgeries, such as Cochlear Implants (CIs), accurate object segmentation can provide useful information for surgeons before an operation. Recently published image segmentation methods that leverage machine learning usually rely on a large number of manually annotated ground truth labels. However, it is a laborious and time-consuming task to prepare the dataset. This paper presents a novel technique using a self-supervised 3D-UNet that produces a dense deformation field between an atlas and a target image that can be used for atlas-based segmentation of the ossicles. Our results show that our method outperforms traditional image segmentation methods and generates a more accurate boundary around the ossicles based on Dice similarity coefficient and point-to-point error comparisons. The mean Dice coefficient is improved by 8.51% with our proposed method. © 2023 SPIE.

关键词： Image segmentation

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Early Discovery of Emerging Entities in Persian Twitter with Semantic Similarity 9

Early Discovery of Emerging Entities in Persian Twitter with...

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9th International Conference on Web Research, ICWR 2023

作者： Yousefi, Shahin Hooshmand, Mohsen Afsharchi, Mohsen Dept. of Computer Science and Information Technology Zanjan Iran University of Zanjan Dept. of Electrical & Computer Engineering Zanjan Iran

ISBN: (纸本)9798350399691

Discovering emerging entities (EEs) is the problem of finding entities before their establishment. These entities can be critical for individuals, companies, and governments. Many of these entities can be discovered on social media platforms, e.g. Twitter. These identities have been the spot of research in academy and industry in recent years. Similar to any machine learning problem, data availability is one of the major challenges in this problem. This paper proposes EEPT that is an online clustering method able to discover EEs without any need for training on a dataset. Additionally, due to the lack of a proper evaluation metric, this paper uses a new metric to evaluate the results. The results show that EEPT is promising and finds significant entities before their establishment. © 2023 IEEE.

关键词： Semantics

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Spatial Dynamics for Identification of Individuals through Gait and Other Locomotion Activities

Spatial Dynamics for Identification of Individuals through G...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Anusha, R. Sanshi, Shridhar Bapuji Institute of Engineering and Technology Dept. of Computer Science and Engineering Davanagere India National Institute of Technology Dept. of Computer Science and Engineering Karnataka Surathkal India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379525

Gait, the pattern of walking, has been extensively studied and various methods have been developed to use it as a biometric for individual recognition. Despite this, the potential to identify individuals through running videos has not been thoroughly explored. The paper introduces a novel method that expands the feature-based approach for identifying individuals based on their running style. This work focuses on extracting the mutual information and location specific metric from the key gait poses of subjects in the testing and training datasets. Later on, the assignment of a testing sample to the training sample is accomplished using the proposed classification method. The experiments are conducted on KTH, OU-ISIR A, and Weizmann database. The efficiency of this method is demonstrated by the obtained experimental results. © 2024 IEEE.

关键词： Feature extraction

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A Distributed Consensus Approach for Power and Water Co-Generation in Microgrids 50

A Distributed Consensus Approach for Power and Water Co-Gene...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： ALhaji, Ahmad Capezza, Skieler Chow, Mo-Yuen Dept. of Electrical and Computer Engineering North Carolina State University NC United States Dept. of Electrical and Computer Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665464543

A freshwater shortage can threaten the well-being of people and communities. With the growing global population and limited access to freshwater resources around the world, desalination has become an important technology for meeting the freshwater demand of communities. Desalination, the process of extracting freshwater from saline water, can be done in a centralized water processing plant or distributed at smaller desalination units. This paper investigates the integration of distributed desalination units within a microgrid for the dual optimization of water and energy generation by developing a distributed consensus algorithm to solve the economic dispatch problem for power and water co-generation. The simulation results validate the algorithm by finding the optimal generation capacity of each element in the microgrid while meeting the dynamic demands of power and water. © 2024 IEEE.

关键词： Microgrids

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An Extended State Observer Based Super-Twisting Sliding Mode Controller for Electrostatic Actuators 50

An Extended State Observer Based Super-Twisting Sliding Mode...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Trovato, Gianfranco Dong, Lili Mandali, Anusree Dept. Electrical and Computer Engineering Cleveland State University ClevelandOH United States Dept. Electrical and Computer Engineering Lafayette College EastonPA United States

ISBN: (纸本)9781665464543

This paper presents a super twisting sliding mode control (STSMC) system to stabilize a parallel-plate electrostatic actuator (ESA) and extend its operating range beyond the pull-in point. A voltage-controlled ESA is stable only within one-third of the full gap due to susceptibility to pull-in, nonlinearity, and sensitivity to electronic noise and disturbances. To address these challenges, an STSMC is developed to overcome the nonlinearity and instability, while an extended state observer (ESO) estimates the ESA's displacement. Implemented on a real ESA model in MATLAB/Simulink, the ESO-based STSMC increases the ESA's travel range to 99.9% of its full gap despite noise, disturbance, and parametric uncertainty. The system's stability is proved using a Lyapunov approach. © 2024 IEEE.

关键词： Sliding mode control

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