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检索条件"机构=DOS in Computer Science"

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Classification of Human Peripheral Blood Cells Using EfficientNet B0-B7 Models

Classification of Human Peripheral Blood Cells Using Efficie...

引用

2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Ramyashree, S.K. Sharada, B. Bhairava, R. DoS in Computer Science Manasagangotri University of Mysore Mysuru India

ISBN: (纸本)9798331528126

Peripheral blood cells are essential for the human immune system's defense against microorganisms that cause disease. Precise classification of these cells is essential for medical diagnostics since they contain significant information that may be evaluated to assess an individual's health status. Accurate blood cell type classification is critical to histopathology because it offers valuable information for detecting diseases like viruses and cancers. In this work, we propose an advanced deep learning (DL) framework based on the EfficientNet series (B0 to B7) for the automatic categorization of peripheral blood cells. Our approach targets eight distinct classes, including basophils, eosinophils, neutrophils, lymphocytes, monocytes, immature granules (IG), erythroblasts and platelets, utilizing two comprehensive datasets: the publicly available Raabin Health Database and the PBC dataset. The proposed EfficientNet models demonstrate superior performance, achieving highly accurate classification of blood cells with significantly fewer epochs and reduced computational time compared to alternative methods. Our method's performance is thoroughly assessed, demonstrating its efficiency as a reliable tool for blood cell analysis in medical applications. © 2024 IEEE.

关键词： Diseases

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A Modelling of Novel Approach for Stock Portfolio Management Using Unsupervised Algorithm and Deep Reinforcement Technique 6th

A Modelling of Novel Approach for Stock Portfolio Management...

引用

6th Computational Methods in Systems and Software, CoMeSySo 2022

作者： Ramya, D. Suresha DoS in Computer Science University of Mysore Mysuru India

ISBN: (纸本)9783031214349

Recent developments in Machine Learning have attracted the attention of the financial community for the development of an optimal strategy in stock markets. The majority of existing approaches introduced a model to maximize return. However, these methods often fail to account to adapt to the constantly changing market circumstances. This paper develops an efficient learning model based on the unsupervised learning and deep reinforcement learning technique to determine an optimal stock portfolio. An unsupervised algorithm is employed to select suitable stocks in the portfolio. An LSTM (Long Short-Term Memory) based agent mechanism is proposed to address the weight assignment problem to each stock in order to minimize the risk and increase portfolio value. The study also presents an intelligent trading algorithm that is flexible to trade on any Indian stock portfolio. The experimental analysis showcases the proposed scheme performs better than the baseline and secured portfolio. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Portfolio optimization Reinforcement learning Risk control Stocks Unsupervised learning

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CNN classification subject to practical aspects of Search and Rescue UAV missions

CNN classification subject to practical aspects of Search an...

引用

2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023

作者： de Aquino, Roberto Douglas Guimaraes Barreto, Joniel Bastos do Nascimento Rodrigues, Aline Verri, Filipe Alves Neto Forster, Carlos Henrique Quartucci SP Sao Jose dos Campos Brazil PPG Electronic and Computer Eng. ITA SP Sao Jose dos Campos Brazil Computer Science Division ITA SP Sao Jose dos Campos Brazil

ISBN: (纸本)9798350348071

The use of Unmanned Aerial Vehicles (UAVs) equipped with classification systems in Search and Rescue missions has increasingly shown great application potential. There are however pratical concerns regarding limited embedded computing power and adapting the classifier performance to the characteristics of a specific mission. The UAVs may acquire a large number of images during a mission and it is difficult for humans to search throughout this material. For this matter, we study the performance of a leaner Convolutional Neural Network model to select images with salient objects and present a tool that can help in decision making regarding the definition of the classification threshold through the analysis of possible operational impacts and consequences of eventual detection errors. We also show that, in this application, Data Augmentation is important to improve the classifier performance. © 2023 IEEE.

关键词： Image classification

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Reinforcement Learning Applied to Very Small Size Soccer Decision-Making, Trajectory Planning and Control In Penalty Kicks 16

Reinforcement Learning Applied to Very Small Size Soccer Dec...

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16th Brazilian Symposium on Robotics and 15th Workshop on Robotics in Education, SBR-WRE 2024

作者： Pires Baldão, Thayna Maximo, Marcos R.O.A. Yoneyama, Takashi Computer Science Division Aeronautics Institute of Technology São José dos Campos Brazil Electronic Engineering Division Aeronautics Institute of Technology São José dos Campos Brazil

ISBN: (纸本)9798350391084

In this work, we propose a Reinforcement Learning (RL) training methodology using Proximal Policy Optimization (PPO) and Curriculum Learning (CL) to make simulated Very Small Size Soccer robots learn to convert penalty kicks against two types of opponents: the "line follow" goalkeeper, which defends the goal by following the goal line, and the offensive goalkeeper, which uses the univector field navigation technique to attack the ball. The RL agents trained using this methodology demonstrated sophisticated penalty kick skills with high conversion rates. The best-evaluated RL agent achieved a conversion rate of over 90% against the "line follow" goalkeeper and of over 71% against the offensive goalkeeper. Comparisons with the univector agent showed that the RL agents significantly outperformed it, with conversion rates 78.71% higher against the "line follow" goalkeeper and 1997.66% higher against the offensive goalkeeper. ©2024 IEEE.

关键词： Reinforcement learning

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Assessing Unfairness in GNN-Based Recommender Systems: A Focus on Metrics for Demographic Sub-Groups 20

Assessing Unfairness in GNN-Based Recommender Systems: A Foc...

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20th International Conference on Web Information Systems and Technologies, WEBIST 2024

作者： Chizari, Nikzad Tajfar, Keywan Moreno-García, María N. Department of Computer Science and Automation University of Salamanca Plaza de los Caídos sn Salamanca37008 Spain College of Science School of Mathematics Statistics and Computer Science Department of Statistics University of Tehran Tehran Iran

ISBN: (纸本)9789897587184

Recommender Systems (RS) have become a central tool for providing personalized suggestions, yet the growing complexity of modern methods, such as Graph Neural Networks (GNNs), has introduced new challenges related to bias and fairness. While these methods excel at capturing intricate relationships between users and items, they often amplify biases present in the data, leading to discriminatory outcomes especially against protected demographic groups like gender and age. This study evaluates and measures fairness in GNN-based RS by investigating the extent of unfairness towards various groups and su bgroups within these systems. By employing performance metrics like NDCG, this research highlights disparities in recommendation quality across different demographic groups, emphasizing the importance of accurate, group-level measurement. This analysis not only sheds light on how these biases manifest but also lays the groundwork for developing more equitable recommendation systems that ensure fair treatment across all user groups. Copyright © 2024 by SCITEPRESS.

关键词： Graph neural networks

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Nash Equilibrium-based Resource Allocation Mechanism in Internet of Vehicles

Nash Equilibrium-based Resource Allocation Mechanism in Inte...

引用

2024 IEEE Latin-American Conference on Communications, LATINCOM 2024

作者： Leal, Matheus P. Junior, Lourenco A. P. Guidoni, Daniel L. Goncalves, Vinicius P. Rocha Filho, Geraldo P. Nakamura, Luis Meneguette, Rodolfo I. Department of Computer Science São José do Rio Preto Brazil Department of Computer Science São José dos Campos Brazil Department of Computer Science Ouro Preto Brazil Department of Computer Science Brasilia Brazil Department of Exact and Technological Sciences BA Vitória da Conquista Brazil Department of Computer System São Carlos Brazil

ISBN: (纸本)9798331521110

An increasing number of research topics, including the Internet of Vehicles, have emerged and developed as a result of 5G and 6G mobile networks. Nonetheless, a number of issues have to be addressed in this context, particularly with regard to applications of the Intelligence Transportation System, which have high mobility rates and are very susceptible to delays. Because of these issues, using traditional vehicular clouds is challenging because the minimal quality of service standards are not satisfied. In order to allocate resources to environments that use cloud and edge simultaneously, we therefore propose METRO, a Nash equilibrium-based resource allocation mechanism. The effectiveness of the approach was verified using a Vehicle-to-Everything simulation environment, which contrasted a straightforward random algorithm with a metaheuristic algorithm called GWO. METRO thus had superior load. © 2024 IEEE.

关键词： Cloud platforms

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Ionospheric scintillation simulation based on neural networks 20

Ionospheric scintillation simulation based on neural network...

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20th International Conference on Smart Technologies, EUROCON 2023

作者： Freitas, Moises J. S. Moraes, Alison O. Costa, Emanoel Maximo, Marcos R. O. A. De S. Faria, Clodoaldo São José dos Campos Brazil Rio de Janeiro Brazil Computer Science Division São José dos Campos Brazil Presidente Prudente Brazil

ISBN: (纸本)9781665463973

Thereis a demand for the development of GNSS positioning processing techniques that are more tolerant to the effects of the low latitude ionosphere (in particular, scintillation). The possibility of simulating scintillating channels supports the development of more sophisticated test benches and receivers. This paper proposes a neural network-based simulator of ionospheric amplitude scintillation. This synthetic scintillation simulator uses autoencoders and generative adversarial networks (GANs) to generate time series that follow the statistical characteristics of the \alpha-\mu fading model. A part of the proposed network tries to create a synthetic signal, similar to the field data. The proposed neural network was trained and validated with scintillation data acquired in Sao Jose dos Campos, Brazil, in February 2012 and November 2014. The results of the proposed method show that the simulator yields the correct values of the scintillation index, and the estimated fading coefficients are also close to the specified values. These aspects show that this kind of approach can be promising in the simulation of fading channels. Future improvements of the model are also be discussed. © 2023 IEEE.

关键词： Fading channels

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Implementation of Firmware Based on Real-time Operating System for Small Size League Robot 16

Implementation of Firmware Based on Real-time Operating Syst...

引用

16th Brazilian Symposium on Robotics and 15th Workshop on Robotics in Education, SBR-WRE 2024

作者： Freiberger, Andrei Maximo, Marcos R.O.A. Loubach, Denis Autonomous Computational Systems Lab SP São José dos Campos Brazil Autonomous Computational Systems Lab Aeronautics Institute of Technology SP São José dos Campos Brazil Computer Science Division Aeronautics Institute of Technology SP São José dos Campos Brazil

ISBN: (纸本)9798350391084

This article presents the implementation of a real-time operating system (RTOS) on ITAndroids Small Size League (SSL) category robots, a significant advancement for the team’s participation in the Robocup SSL. The transition to using the STM32H753BI microcontroller and FreeRTOS on the second-generation Mainboard enhances temporal predictability and determinism, crucial for real-time systems. This development allows more efficient task management, including motor control, kicker charging, telemetry transmission, and fault handling, leading to improved robot performance and reliability during matches. The detailed system description and task implementation showcase how the Mainboard efficiently handles various functions such as motor control in a constant rate, responsive kicking mechanisms, and real-time telemetry collection and transmission. The work contributes by proposing a real-time operating system for a robot designed to participate in the RoboCup Small Size League (SSL). Despite the implementation being dependent on our team’s design decisions, the concepts presented here can be adapted by other teams. ©2024 IEEE.

关键词： Firmware

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Quantifying Fairness Disparities in Graph-Based Neural Network Recommender Systems for Protected Groups 19

Quantifying Fairness Disparities in Graph-Based Neural Netwo...

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19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Chizari, Nikzad Tajfar, Keywan Shoeibi, Niloufar Moreno-García, María N. Department of Computer Science and Automation Science Faculty University of Salamanca Plaza de los Caídos sn Salamanca37008 Spain College of Science School of Mathematics Statistics and Computer Science Department of Statistics University of Tehran Tehran Iran

ISBN: (纸本)9789897586729

The wide acceptance of Recommender Systems (RS) among users for product and service suggestions has led to the proposal of multiple recommendation methods that have contributed to solving the problems presented by these systems. However, the focus on bias problems is much more limited. Some of the most successful and recent methods, such as Graph Neural Networks (GNNs), present problems of bias amplification and unfairness that need to be detected, measured, and addressed. In this study, an analysis of RS fairness is conducted, focusing on measuring unfairness toward protected groups, including gender and age. We quantify fairness disparities within these groups and evaluate recommendation quality for item lists using a metric based on Normalized Discounted Cumulative Gain (NDCG). Most bias assessment metrics in the literature are only valid for the rating prediction approach, but RS usually provide recommendations in the form of item lists. The metric for lists enhances the understanding of fairness dynamics in GNN-based RS, providing a more comprehensive perspective on the quality and equity of recommendations among different user groups. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Recommender systems

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Classification of Human Peripheral Blood Cells Using EfficientNet B0-B7 Models

Classification of Human Peripheral Blood Cells Using Efficie...

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Smart Electronics and Communication Systems (ISENSE), International Conference on

作者： Ramyashree K S Sharada B Bhairava R DoS in Computer Science Manasagangotri University of Mysore Mysuru India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

关键词： Accuracy Microorganisms Histopathology Computational modeling Lymphocytes Cells (biology) Robustness Topology Medical diagnosis Blood

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