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Congress on Evolutionary Computation

作者： Kosei Kobayashi Masayoshi Aritsugi Pedro Henrique González Israel Mendonça Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Hyperparameter optimization (HPO) is paragon to maximize performance when designing machine learning models. Among different HPO methods, Genetic Algorithm (GA) based optimization is considered effective because it allows a wide and diverse range of solutions to be explored. However, GA's exploratory nature makes this type of algorithm to evaluate many solutions that do not improve the overall performance. This is specially costly when the objective function to be evaluated is time-consuming, like in the HPO field. In this paper, we propose an efficient hybrid algorithm that is able to reduce computational cost by combining deep reinforcement learning with the Biased Random Key Genetic Algorithm (BRKGA), a variant of genetic algorithms. Our reinforcement learning agent has a decision-making role during the population's fitness calculation, in which it filters out chromosomes that would not improve the overall fitness of the population. The agent uses small amounts of pre-trained data to identify trends in potentially good solutions, and carry out its decision process. We conduct experiments on eight different datasets to assess the effectiveness of the proposed method, and the results show that the proposed method can significantly reduce the computation time of hyperparameter search using BRKGA (up to 44% reduction in computational time) without compromising the quality of the solution (no statistically difference in results).

关键词： Machine learning algorithms Filters Sociology Evolutionary computation Hyperparameter optimization Market research Linear programming

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Efficient Node Reduction Heuristic for GNN-Based Traffic Speed Forecasting

Efficient Node Reduction Heuristic for GNN-Based Traffic Spe...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Yuto Inokuchi Pedro Henrique González Masayoshi Aritsugi Israel Mendonça Graduate School of Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, classification, and clustering, due to their ability to learn node and edge attributes and relationships, and they have been utilized for intelligent transportation systems recently by converting sensor networks into graph structures. Deep spatio-temporal neural networks, including Spatio-Temporal Graph Convolutional Networks (STGCNs), capture spatial and temporal dependencies, making them suitable for traffic speed forecasting, traffic demand prediction, and travel time estimation. Despite their success, GNNs face challenges in industrial applications due to significant memory usage and time consumption. In this paper, we propose a new approach to node reduction that outperforms existing methods in computational efficiency. Our experiments on two real-world traffic datasets demonstrate that using the heuristic and edge information to reduce nodes can cut computation time of optimization up to 95% and, by eliminating noise, can even enhance prediction accuracy.

关键词： Graph convolutional networks Noise Memory management Estimation Computational efficiency Forecasting Optimization Intelligent transportation systems Faces Genetic algorithms

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DeepCell: Multiview Representation Learning for Post-Mapping Netlists

arXiv

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arXiv 2025年

作者： Shi, Zhengyuan Ma, Chengyu Zheng, Ziyang Zhou, Lingfeng Pan, Hongyang Jiang, Wentao Yang, Fan Yang, Xiaoyan Chu, Zhufei Xu, Qiang Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China School of Computer Science Hangzhou Dianzi University Hangzhou China School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University Shanghai China National Center of Technology Innovation for EDA Nanjing China

Representation learning for post-mapping (PM) netlists is a critical challenge in Electronic Design Automation (EDA), driven by the diverse and complex nature of modern circuit designs. Existing approaches focus on intermediate representations like And-Inverter Graphs (AIGs), limiting their applicability to post-synthesis stages. We introduce DeepCell, a multiview representation learning framework that integrates structural and functional insights from both PM netlists and AIGs to learn rich, generalizable embeddings. At its core, DeepCell employs the novel Mask Circuit Modeling (MCM) mechanism, which refines PM netlist representations in a self-supervised manner using pretrained AIG encoders. DeepCell sets a new benchmark in PM netlist representation, outperforming existing methods in predictive accuracy and reconstruction fidelity. To validate its efficacy, we apply DeepCell to functional engineering Change Orders (ECO), achieving significant reductions in patch generation costs and runtime while improving patch quality. Copyright © 2025, The Authors. All rights reserved.

关键词： Mapping

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Deep learning-based real time detection for cardiac objects with fetal ultrasound video

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Informatics in Medicine Unlocked 2023年 36卷

作者： Iriani Sapitri, Ade Nurmaini, Siti Naufal Rachmatullah, Muhammad Tutuko, Bambang Darmawahyuni, Annisa Firdaus, Firdaus Rini, Dian Palupi Islami, Anggun Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia Doctoral Program Faculty of Engineering Universitas Sriwijaya Palembang 30139 Indonesia Department of Informatics Engineering Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia

Fetal cardiac anatomical structure interpretation by ultrasound (US) is a key part of prenatal assessment. Unfortunately, the numerous speckles in US video, the small size of fetal cardiac structures, and unfixed fetal positions make manual detection processes difficult. To alleviate such problems, a deep learning model was developed to fully automate the processing of fetal echocardiographs with 2-dimensional cross-sectional images. The weakness of such an approach is that a physician immediately interprets US video with 3-dimensional objects to diagnose patients in clinical practice. To improve fetal cardiac anatomical structure interpretation via US for accurate and real time diagnosis, this paper proposes a real-time fetal cardiac substructure detection using US video with the You Only Look Once (YOLO) framework. In YOLO, an end-to-end neural network makes predictions for cardiac substructure objects, boxes, and class probabilities simultaneously. To achieve reliable performance, 40 fetal echocardiography videos were trained with the new YOLOv7 architecture and fine-tuned to work optimally and run efficiently. We conducted with nine fetal cardiac substructure objects such as the left atrium, right atrium, left ventricle, right ventricle, tricuspid valve, pulmonary valve, mitral valve, aortic valve, and aorta. The results yielded the highest mean average precision of 82.10%, reaching 17 frames per second (FPS) for nine cardiac substructure objects in 0.3 ms. The main finding of our study is that with even a limited number of US videos, YOLOv7 can detect fetal cardiac substructure objects in real-time. Such a network can work efficiently to detect small fetal cardiac objects automatically in a rapid phase with the help of proper fine-tuning. This work mainly assists medical experts in the fetal cardiac anatomy diagnostic process. © 2022 The Authors

关键词： computer vision Fetal echocardiography Object detection YOLOv7

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Mudahnya BM: A Context-Aware Mobile Cloud Learning Application Using Semantic-Based Approach

Mudahnya BM: A Context-Aware Mobile Cloud Learning Applicati...

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7th International Conference on Advances in Visual Informatics, IVIC 2021

作者： Muhammad, Sufri Admodisastro, Novia Osman, Hafeez Mohd Ali, Norhayati Faculty of Computer Science and Information Technology Department of Software Engineering and Information System Universiti Putra Malaysia Seri Kembangan Selangor43400 Malaysia

ISBN: (纸本)9783030902346

A great potential of various learning environment in mobile learning application can clearly be seen in current pandemic situation. The accessibility of the learning resources needs to be available from anywhere anytime despite having strong or poor internet connection. It has motivated researchers to imply context-aware capability in improving the accessibility of the learning resources. This paper presents a development process of Mudahnya BM mobile application that follows a fundamental concept of Mobile Cloud Learning (MCL). Mudahnya BM is an application to learn basic Malay language for learner 7–12 years old. The injection of extrinsic and intrinsic context-aware help the application to improve the reasoning process for finding available learning resources from service providers. Semantic-based approach is applied in the reasoning process. This study involved the end users for evaluation purposes. 33 randomized scenarios have been tested using One-Sample Wilcoxon Signed Rank test. The result shows a positive impact to the population. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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Designing of a Solar MPPT Controller with the Technique of Differentiation Between Uniform Irradiance and Partial Shading Conditions 9

Designing of a Solar MPPT Controller with the Technique of D...

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9th IEEE International Conference on Power and Energy, PECon 2022

作者： Ahmed, Sajib Mekhilef, Saad Mubin, Marizan Tey, Kok Soon Rawa, Muhyaddin University of Malaya Power Electronics and Renewable Energy Research Laboratory Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia University of Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Jeddah21589 Saudi Arabia

ISBN: (数字)9781665409902

ISBN: (纸本)9781665409902

Solar energy is considered one of the best essential energy sources to solve the present power crisis. The solar photovoltaic system has a low conversion efficiency that inflicts maximum power extractions. The non-linear relationship between the voltage and current insists on using a maximum power point tracking (MPPT) controller. Perturb and observe (P&O) MPPT controller is popular because of its simple design and cost-effectiveness features;nonetheless, it has drawbacks while operating under partial shading conditions (PSC). The maximum power trapezium (MPT) method modified the P&O algorithm by incorporating an appropriate scanning mechanism for PSC. Despite having good PSC performance, this method's tracking time is much longer than the conventional algorithm in both uniform irradiance conditions (UIC) and PSC. The algorithm proposed in this paper modifies the MPT method with an extra strategy to distinguish between the UIC and PSC. If the pattern is recognized as UIC, the proposed method utilizes the conventional P&O algorithm to track the MPP;otherwise, scanning techniques are being used. The proposed algorithm's operation was tested on simulation using distinct UIC and PSC patterns and worked effectively. © 2022 IEEE.

关键词： Cost effectiveness

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Restoring Lesions in Low-Dose Computed Tomography Images of COVID-19 Using Deep Learning 6th

Restoring Lesions in Low-Dose Computed Tomography Images of ...

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6th Kuala Lumpur International Conference on Biomedical engineering, BioMed 2021

作者： Kulathilake, K. A. Saneera Hemantha Abdullah, Nor Aniza Lachyan, Abhishek Shivanand Bandara, A. M. Randitha Ravimal Patel, Dhrumil Deveshkumar Lai, Khin Wee Faculty of Computer Science and Information Technology Department of Computer System and Technology Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Medicine Department of Social and Preventive Medicine Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Applied Sciences Department of Computer Science University of Sri Jayewardenepura Nugegoda Sri Lanka Department of Radiology Seth GS Medical College King Edward Memorial Hospital Acharya Donde Marg Parel East Maharastra Parel Mumbai400012 India Faculty of Engineering Department of Biomedical Engineering Universiti Malaya Kuala Lumpur50603 Malaysia

ISBN: (纸本)9783030907235

The use of Low-dose Computed Tomography (LDCT) in clinical medicine for diagnosis and treatment planning is widespread due to the minimal exposure of patients to radiation. Also, recent studies have confirmed that LDCT is a feasible medical imaging modality for diagnosing COVID-19 cases. In general, X-ray tube current is being reduced to acquire the LDCT images. Reduction of the X-ray flux introduces the Quantum noise into the generated LDCT images and, as a result, it produces visually low-quality CT images. Therefore, it is challenging to differentiate the lesions in the diagnosis of COVID-19 patients using the LDCT images due to low contrast and failure to preserve the subtle structures. Therefore, in this study, we proposed a Deep Learning (DL) model based on the Generative Adversarial Network (GAN) for post-processing the LDCT images to enhance their visual quality. In this proposed model, the generator network is designed as a U-net to generate the restored CT images by filter out the noise. Also, the discriminator network follows a patch-GAN model to discriminate the real and generated images while preserving the texture details. The quantitative and qualitative results demonstrated the effectiveness of noise suppression and structure preservation of the proposed DL method. Hence, it provides an acceptable quality improvement for LDCT images to discriminate the lesions for diagnosing the COVID-19 positive cases. © 2022, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Sasirangan Cloth Recognition and Shopping Experience Simulation Based on Virtual Reality Game with Non-Player Character Integration 8

Sasirangan Cloth Recognition and Shopping Experience Simulat...

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8th International HCI and UX Conference in Indonesia, CHIuXiD 2022

作者： Subandi, Subandi Syahidi, Aulia Akhrian Zakiah, Siti Kiyokawa, Kiyoshi Riyadi, Ahmad Noor, M. Helmy Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin Indonesia Universitas Brawijaya Informatics Engineering Undergraduate Program Faculty of Computer Science Malang Indonesia Nara Institute of Science and Technology Cybernetics and Reality Engineering Laboratory Ikoma Japan Politeknik Negeri Banjarmasin Informatics Engineering Diploma Program Banjarmasin Indonesia

ISBN: (纸本)9781665476645

Sasirangan cloth is one of the traditional cloths owned by Indonesia and is a typical cloth originating from the province of South Kalimantan. This Sasirangan cloth has many motifs and is unique. Sasirangan cloth is also one of the potentials that can attract local and foreign tourists to visit South Kalimantan. However, in recent years, tourist visits to South Kalimantan have decreased due to the impact of the COVID-19 pandemic. Then it also has an impact on the delay in the implementation of the Banjarmasin Sasirangan Festival which is usually held annually. The next impact is a decrease in turnover and interest in shopping for Sasirangan cloth, so that employees who work for Sasirangan cloth craftsmen are dismissed and reduce people's income. So the purpose of this study is to report the results of the development of a Virtual Reality Game application called VR-SasiranganKu by integrating the interaction of Non-Player Character (NPC) behavior with the type of narrative in the simulation genre game. VR-SasiranganKu is recommended to introduce and at the same time promote Sasirangan cloth to users, then justify the reaction to knowledge and shopping experience, and evaluate user experience using the UX Honeycomb method. Respondents involved as many as 100 people who are local and national tourists. The results show that the interaction of NPC behavior can provide good direction to users regarding the introduction of Sasirangan cloth by presenting an appropriate interaction design based on the narrative and the theme of the NPC that has been adopted, the results of justification using VR-SasiranganKu can provide knowledge reaction and very good shopping experience, and based on user experience evaluation that VR-SasiranganKu has adopted a very good UX with an average score of 4.91. © 2022 IEEE.

关键词： Virtual reality

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Heuristic evaluation and analytical network process for analyzing the role of websites in higher education 10

Heuristic evaluation and analytical network process for anal...

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10th International Seminar on New Paradigm and Innovation of Natural sciences and itsApplication, ISNPINSA 2020

作者： Yani, F. Nurhayati, O.D. Widodo, A.P. Department of Information System Post Graduated School Diponegoro University Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Indonesia

It is currently thought that university websites play important roles in maintaining communication with stakeholders. The evidence shows that the usability aspect of the website positively correlates with governance support. This paper presents a new approach to identify, assess and evaluate usability aspect base on ten Heuristic Evaluation principles by implementing the ANP method. This method works by determining the weight of the best site criteria according to Webometric version ranking. The initial questionnaire was tested for validity and reliability using total item correlation analysis, the r-value was obtained in the range 0.706-0.919 (valid) and the reliability value in the range 0.861-0.939 (reliable). It was also found that by implementing expert judgment tests, some questionnaires are required more editorial adjustment. Interestingly, the layout of the websites hit the usability criteria. Another ten HE aspects run well while some light improvements were required for error prevention, recognition rather than recall, help users recognize, diagnose, and recover from errors aspects. The most remarkable result to emerge from the data was that visibility of system status, match between system and the real world, and consistency standards shown good result. © Published under licence by IOP Publishing Ltd.

关键词： Heuristic methods

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Decision support system for handling intervention on toddlers stunting cases in Indonesia using the certainty factor method 10

Decision support system for handling intervention on toddler...

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10th International Seminar on New Paradigm and Innovation of Natural sciences and itsApplication, ISNPINSA 2020

作者： Sutoyo, I.W. Widodo, A.P. Rochim, A.F. Department of Information System Post Graduated School Diponegoro University Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Indonesia Department of Computer Engineering Faculty Engineering Diponegoro University Indonesia

Stunting is a condition of failure to thrive in children under five caused by lack of nutritional intake for a long time and the occurrence of repeated infections. The causative factor is influenced by inadequate parenting, especially in the first 1,000 days of life. This study applies a decision support system with an expert system as an intervention for handling stunting by providing solutions using the Certainty Factor method. This expert system is implemented using the PHP programming language, and the use of expert systems will be easier when implemented into web-based applications. The test results are acceleration in reducing the number of stunting cases, thus providing the right percentage and solution in handling and increasing supervision of government efforts to combat stunting. Stunting intervention research can see the results of the percentage value of sensitive and specific interventions for pregnant women, nursing mothers, and adolescent girls. Testing uses black-box validation and accuracy. The validation results are in the form of 100%, which shows the system functionality can run well and according to the list of requirements. The accuracy result is 83% which shows that the expert system can function quite well according to the Certainty Factor method. © Published under licence by IOP Publishing Ltd.

关键词： Decision support systems

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