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Integrating transformer and graph attention network for circRNA-miRNA interaction prediction

IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Wei, Meng-Meng Wang, Lei Zhao, Bo-Wei Su, Xiao-Rui You, Zhu-Hong Huang, De-Shuang China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Southwest University College of Computer and Information Science School of Software Chongqing400715 China Harvard Medical School Department of Biomedical Informatics BostonMA02115 United States Guangxi Academy of Science Nanning530007 China Eastern Institute of Technology Eastern Institute for Advanced Study Ningbo315200 China

CircRNA-miRNA interaction (CMI) plays a crucial role in the gene regulatory network of the cell. Numerous experiments have shown that abnormalities in CMI can impact molecular functions and physiological processes, leading to the occurrence of specific diseases. Current computational models for predicting CMI typically focus on local molecular entity relationships, thereby neglecting inherent molecular attributes and global structural information. To address these limitations, we propose a multi-feature fusion prediction model based on the transformer and graph attention network, named EGATCMI. Specifically, EGATCMI combines the transformer architecture with Word2vec to pre-train the sequence of circRNA and miRNA, capturing their sequence feature representation and sequence similarity. By leveraging the self-attention mechanism, EGATCMI extracts global structural feature from the CMI network. EGATCMI effectively integrates the obtained multi-feature for prediction, achieving AUC values of 0.9106 and 0.9470 on the CMI-9905 and CircBank datasets, respectively, outperforming existing methods. In case studies that the prediction of interactions between three miRNAs that are closely related to diseases and circRNAs, 8 out of 10 pairs were accurately predicted and validated. Extensive experimental results demonstrate the potential of EGATCMI as a reliable tool for candidate screening in biological investigations. © 2013 IEEE.

关键词： Prediction models

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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Expanding Crack Segmentation Dataset with Crack Growth Simulation and Feature Space Diversity

Expanding Crack Segmentation Dataset with Crack Growth Simul...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Qin Lei Rui Yang Jiang Zhong Rongzhen Li Muyang He Mianxiong Dong Kaoru Ota College of Computer Science Chongqing University Chongqing China School of Computer Science Shaanxi Normal University Xi’an China The School of Advanced Technology Xi’an Jiaotong-Liverpool University Xi’an China Department of Sciences and Informatics Muroran Institute of Technology Muroran Japan

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

In this paper, we address the significant challenge of data scarcity in the field of crack segmentation, a key aspect of structural health monitoring. To tackle this, we introduce the CrackGrowDiff framework, an innovative approach for expanding crack datasets. Utilizing a two-stage controllable generation process that combines a random walk algorithm and semantic diffusion models, our framework minimizes discrepancy of misalignment between synthetic data and original data while enhancing data informativeness. We further ensure the quality and informativeness of synthetic data through feature space diversity, employing a pre-trained Variational Autoencoder (VAE) for selection based on Kullback-Leibler (KL) divergence. Comparative experiments demonstrate CrackGrowDiff’s superiority over traditional data augmentation and GANs-based methods, making it a substantial advancement in addressing the data scarcity in crack segmentation tasks. A DEMO and related code will be made public: https://***/spaces/QinLei086/Two-stage-SDM-for-crack-dataset-expending

关键词： Spatial diversity Image edge detection Semantics Process control Diffusion models Data augmentation Data models

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A survey on task scheduling and optimization techniques for IoT-enabled UAV with Edge / Fog computing

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Telecommunication Systems 2025年第3期88卷 1-33页

作者： Satouf, Aram Hamidoğlu, Ali Gul, Omer Melih Kuusik, Alar Kadry, Seifedine Nimer Elghirani, Ali Department of Computer Engineering Bahcesehir University Istanbul Türkiye Department of Mathematics Bahcesehir University Istanbul Türkiye ILMEE Department of Mathematical and Statistical Sciences University of Alberta Edmonton Canada Informatics Institute Istanbul Technical University Istanbul Türkiye School of Information Technology Tallinn University of Technology Tallinn Estonia Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Faculty of Information Technology Libyan International Medical University Benghazi Libya

The Internet of Things (IoT) and cloud computing are two technologies that are rapidly growing and have the potential to change many different industries. IoT devices provide real-time data gathering and analysis, enabling organizations to make data-driven choices. Cloud computing offers a scalable and flexible platform for storing and processing the enormous amounts of data IoT devices create. However, several issues like excessive latency, network inefficiency, and security concerns have arisen due to the centralized architecture of cloud computing, particularly in IoT applications where strict real-time performance and operational reliability requirements are present including intelligent transportation systems (ITS), unmanned ground and aerial vehicles (UGVs, UAVs). Edge computing, a new architecture that tries to decentralize data processing from the cloud to the network edge, has been presented as a solution to these problems. In this survey, we investigate the role of cloud, fog, and edge computing in the smart environment containing wireless sensor networks (WSNs) and mobile IoT devices, especially UAVs. Integration of optimization algorithms and meta-heuristic techniques are provided in the context of IoT applications. Furthermore, we discuss the benefits of fog and edge computing, use cases, and the Quality of Service (QoS) used for task scheduling.

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Comparative Analysis of Robotic Arm Efficiency: Evaluating PID and Fuzzy-PID Control using Printed Mechanical Structure and ESP8266 Integration

Comparative Analysis of Robotic Arm Efficiency: Evaluating P...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Padma Nyoman Crisnapati I Made Darma Susila I Komang Agus Ady Aryanto Jaturapith Krohkaew Ricky Aurelius Nurtanto Diaz I Made Suandana Astika Pande Department of Information Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Department of Computer Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Dept. of Big Data Management and Analytics Faculty of Science and Technology Rajamangala University of Technology Thanyaburi Thanyaburi Thailand

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This study investigates the comparative effectiveness of manual PID tuning versus Fuzzy-PID control in enhancing the performance of a robot arm with two degrees of freedom (2-DOF). The experimental approach involves conducting two scenarios: manual tuning of PID parameters in Scenario 1 and utilizing a fuzzy algorithm for PID parameter tuning in Scenario 2. The novelty of this research lies in the new 3D mechanical design of the 2-DOF robot arm and the utilization of the ESP8266 microcontroller for implementing Fuzzy-PID control. Performance evaluation is conducted using Root Mean Square Error (RMSE) and Root Mean Square Percentage Error (RMSPE) calculations. The results reveal that Fuzzy-PID control significantly reduces errors compared to manual PID tuning, with lower RMSE and RMSPE values indicating more precise and stable control. These findings underscore the potential of integrating fuzzy logic with PID control to enhance system adaptability and flexibility. Additionally, the ESP8266 is capable of handling the computations required for fuzzy-PID control while being cost-effective.

关键词： Fuzzy logic PI control Microcontrollers 2-DOF Manuals Manipulators Three-dimensional printing PD control Root mean square Tuning

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Mobility-Aware Multi-Task Decentralized Federated Learning for Vehicular Networks: Modeling, Analysis, and Optimization

arXiv

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arXiv 2025年

作者： Chen, Dongyu Deng, Tao Huang, He Jia, Juncheng Dong, Mianxiong Yuan, Di Li, Keqin School of Computer Science and Technology Soochow University Jiangsu Suzhou215006 China Department of Sciences and Informatics Muroran Institute of Technology Muroran050-8585 Japan Department of Information Technology Uppsala University Uppsala751 05 Sweden Department of Computer Science State University of New York New PaltzNY12561 United States

Federated learning (FL) is a promising paradigm that can enable collaborative model training between vehicles while protecting data privacy, thereby significantly improving the performance of intelligent transportation systems (ITSs). In vehicular networks, due to mobility, resource constraints, and the concurrent execution of multiple training tasks, how to allocate limited resources effectively to achieve optimal model training of multiple tasks is an extremely challenging issue. In this paper, we propose a mobility-aware multi-task decentralized federated learning (MMFL) framework for vehicular networks. By this framework, we address task scheduling, subcarrier allocation, and leader selection, as a joint optimization problem, termed as TSLP. For the case with a single FL task, we derive the convergence bound of model training. For general cases, we first model TSLP as a resource allocation game, and prove the existence of a Nash equilibrium (NE). Then, based on this proof, we reformulate the game as a decentralized partially observable Markov decision process (DEC-POMDP), and develop an algorithm based on heterogeneous-agent proximal policy optimization (HAPPO) to solve DEC-POMDP. Finally, numerical results are used to demonstrate the effectiveness of the proposed algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Fuzzy control system design for wheel slip prevention and tracking of desired speed profile in electric trains

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Asian Journal of Control 2022年第1期24卷 388-400页

作者： Moaveni, Bijan Rashidi Fathabadi, Fatemeh Molavi, Ali Center of excellence for Modelling and Control of Complex Systems Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Western Michigan University MI United States Institute of Robotics and Industrial Informatics Polytechnical University of Catalonia Barcelona Spain

This study proposes an anti-slip control system for electric trains based on the fuzzy logic theory, which prevents the wheels from slipping during the acceleration and simultaneously tracks the desired speed profile. To improve the control performance, the train longitudinal velocity and the slip ratio are estimated. By using a field-oriented control (FOC), the angular speed of the traction motor is controlled. The fuzzy control system determines the desired angular speed of the traction motor as the reference input of FOC to obtain the desired slip ratio and track the desired speed profile. Simulation results show the effectiveness of the control system in various wheel–rail surface conditions based on the real parameters of ER24PC locomotive. © 2020 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd

关键词： Traction motors

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On the Uncrossed Number of Graphs

arXiv

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arXiv 2024年

作者： Balko, Martin Hliněný, Petr Masařík, Tomáš Orthaber, Joachim Vogtenhuber, Birgit Wagner, Mirko H. Department of Applied Mathematics Faculty of Mathematics and Physics Charles University Czech Republic Faculty of Informatics Masaryk University Brno Czech Republic Institute of Informatics Faculty of Mathematics Informatics and Mechanics University of Warsaw Poland Institute of Software Technology Graz University of Technology Austria Institute of Computer Science Osnabrück University Germany

Visualizing a graph G in the plane nicely, for example, without crossings, is unfortunately not always possible. To address this problem, Masařík and Hliněný [10] recently asked for each edge of G to be drawn without crossings while allowing multiple different drawings of G. More formally, a collection D of drawings of G is uncrossed if, for each edge e of G, there is a drawing in D such that e is uncrossed. The uncrossed number unc(G) of G is then the minimum number of drawings in some uncrossed collection of G. No exact values of the uncrossed numbers have been determined yet, not even for simple graph classes. In this paper, we provide the exact values for uncrossed numbers of complete and complete bipartite graphs, partly confirming and partly refuting a conjecture posed by Hliněný and Masařík [10]. We also present a strong general lower bound on unc(G) in terms of the number of vertices and edges of G. Moreover, we prove NP-hardness of the related problem of determining the edge crossing number of a graph G, which is the smallest number of edges of G taken over all drawings of G that participate in a crossing. This problem was posed as open by Schaefer in his book [18].MSC Codes 68R10 Copyright © 2024, The Authors. All rights reserved.

关键词： Graph theory

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MetaSignal: Meta Reinforcement Learning for Traffic Signal Control via Fourier Basis Approximation

TechRxiv

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TechRxiv 2023年

作者： Huang, Shuning Ota, Kaoru Dong, Mianxiong Zhou, Huan Department of Sciences and Informatics Muroran Institute of Technology Hokkaido Muroran Japan College of Computer and Information Technology China Three Gorges University Yichang China

Traffic signal control plans significantly impact transportation system efficiency by regulating traffic conditions at intersections. Adaptive traffic plans that can adjust to real-time road conditions are more effective as a result. Reinforcement learning succeeds at adapting strategies based on feedback derived from the environment, and is thus proficient in dealing with complex traffic scenarios that change dynamically. However, current RL methods rely on significant computational periods to obtain precise functioning mechanisms within the scenarios, posing barriers to their adoption for new scenarios. In addition to directly optimizing the RL model itself to enable fast learning from scratch, another idea is to make the model transferable or reusable with the learned experience. Given the diversity of migration scenarios, the underlying control algorithm should guarantee convergence and endeavor to be parameter-insensitive. From the above concern, we proposed MetaSignal, an efficient meta-reinforcement learning method for traffic signal control. Specifically, our approach utilizes the Fourier basis as the value function approximation in reinforcement learning, distinguishing it from methods like neural network approximation. This linear approximation offers advantages such as convergence facilitation, error bound achievement, and reduced parameter dependence. The meta-learning framework adopts a model-agnostic approach, enabling effective adaptation of the base model to the target scenario with limited training cost. Empirically, the proposed method shows promising and stable performance for traffic signal control through comprehensive comparison experiments in both synthetic and real-world traffic networks. © 2023, CC BY.

关键词： Reinforcement learning

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EffNet-CNN:A Semantic Model for Image Mining&Content-Based Image Retrieval

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computer Modeling in Engineering & Sciences 2025年第5期143卷 1971-2000页

作者： Rajendran Thanikachalam Anandhavalli Muniasamy Ashwag Alasmari Rajendran Thavasimuthu Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Informatics and Computer Systems College of Computer ScienceKing Khalid University Abha61421Saudi Arabia Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia Department of Sustainable Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai602105India

Content-Based Image Retrieval(CBIR)and image mining are becoming more important study fields in computer vision due to their wide range of applications in healthcare,security,and various *** image retrieval system mainly relies on the efficiency and accuracy of the classification *** research addresses the challenge of enhancing the image retrieval system by developing a novel approach,EfficientNet-Convolutional Neural Network(EffNet-CNN).The key objective of this research is to evaluate the proposed EffNet-CNN model’s performance in image classification,image mining,and *** novelty of the proposed EffNet-CNN model includes the integration of different techniques and *** model includes the Mahalanobis distance metric for feature matching,which enhances the similarity *** model extends EfficientNet architecture by incorporating additional convolutional layers,batch normalization,dropout,and pooling layers for improved hierarchical feature extraction.A systematic hyperparameter optimization using SGD,performance evaluation with three datasets,and data normalization for improving feature *** EffNet-CNN is assessed utilizing precision,accuracy,F-measure,and recall metrics across MS-COCO,CIFAR-10 and 100 *** model achieved accuracy values ranging from 90.60%to 95.90%for the MS-COCO dataset,96.8%to 98.3%for the CIFAR-10 dataset and 92.9%to 98.6%for the CIFAR-100 dataset.A validation of the EffNet-CNN model’s results with other models reveals the proposed model’s superior *** results highlight the potential of the EffNet-CNN model proposed for image classification and its usefulness in image mining and CBIR.

关键词： Image mining CBIR semantic features EffNet-CNN image retrieval

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