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Classification of Breast Cancer Molecular Subtypes with Grouping-Scoring-Modeling Approach that Incorporates Disease-Disease Association Information

Classification of Breast Cancer Molecular Subtypes with Grou...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Emma Qumsiyeh Burcu Bakir-Gungor Malik Yousef Department of Computer Science and Information Technology Al-Quds University Jerusalem Palestine Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri Turkey Department of Information Systems Galilee Digital Health Research Center Zefat Academic College Zefat Israel

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

This study uses modern sequencing technology and large biological databases to investigate the molecular intricacies of complicated diseases like cancer. Using gene expression databases and biomarkers, the research aims to improve breast cancer molecular subtype identification for better patient outcomes. Using BRCA LumAB_Her2Basal dataset, this study compares an integrative machine learning-based strategy (GediNET) to traditional feature selection approaches across machine learning classifiers. GediNET excels at uncovering crucial disease-disease connections and potential biomarkers using the Grouping-Scoring-Modeling (GSM) approach, which favors gene groupings above individual genes. Our comparative analysis highlights GediNET’s exceptional performance, notably in terms of accuracy and Area Under the Curve metrics, underscoring its effectiveness in uncovering the genetic intricacies of breast cancer. GediNET’s promise to improve disease classification and biomarker identification by improving biological mechanism understanding goes beyond exceeding traditional approaches. The work shows that GediNET’s integrative method can promote bioinformatics research by identifying the most informative genes associated with certain diseases, enabling focused and customized medicine.

关键词： Measurement Sequential analysis Databases Machine learning Biomarkers Signal processing Genetics

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Detecting Characteristic Points for the Analysis of Bioimpedance Signal Through a Synergy of Fuzzy Rule-based models and Granular Neural Networks

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IEEE Transactions on Fuzzy systems 2024年

作者： Wang, Dan Richter, Monika Zhu, Xiubin Pedrycz, Witold Gacek, Adam Sobotnicki, Aleksander Li, Zhiwu Xi'an University of Science and Technology College of Computer Science and Technology Xi'an710054 China Center for Biomedical Engineering Lukasiewicz Research Network-Krakow Institute of Technology Zabrze41-800 Poland Xidian University School of Mechano-Electronic Engineering Xi'an710071 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey Macau University of Science and Technology Institute of Systems Engineering Taipa China

In this study, we propose a novel methodology for determining accurate positions of characteristic points encountered in the analysis of bioimpedance signals. The proposed approach fully utilizes two fundamental modeling pursuits based on fuzzy rule-based models and neural networks. We take advantages of the unique capabilities of fuzzy rule-based models to characterize the nonlinear relationship between the acquired bioimpedance signals and its temporal coordinate. The fuzzy modeling approach is used to approximate the process that generates the bioimpedance signals through a collection of rules (if-then statements). In the sequel, the parameters of the models are used as the inputs of neural network models to determine the position of characteristic points. We further augment the numeric neural network to its granular counterpart to accommodate the uncertainty in the available experimental evidence through allocating a certain level of information granularity across the parameter space. The resulting granular outputs (intervals) become reflective of the quality and a level of confidence associated with the prediction results. The quality of the prediction results is quantified in terms of the coverage and specificity criteria. The performance index is also enhanced to deal with the situation when the positions provided by experts are also information granules (intervals). The performance of the proposed approach is justified through a collection of experiments carried out on the collected real-world bioimpedance signals. Experimental results show that the proposed approach achieved higher accuracy in determining the position of characteristic points in comparison with other existing methods. © 1993-2012 IEEE.

关键词： Fuzzy models

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A review of Implementation and Challenges of Unmanned Aerial Vehicles for Spraying Applications and Crop Monitoring in Indonesia

arXiv

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arXiv 2023年

作者： Fikri, Muhamad Rausyan Candra, Taufiq Saptaji, Kushendarsyah Noviarini, Ajeng Nindi Wardani, Dilla Ayu Automation Technology and Mechanical Engineering Faculty of Engineering and Science Tampere University Tampere33720 Finland Information Systems Faculty of Engineering and Technology Sampoerna University Jakarta12780 Indonesia Industrial Engineering Faculty of Engineering and Technology Sampoerna University Jakarta12780 Indonesia Mechanical Engineering Faculty of Engineering and Technology Sampoerna University Jakarta12780 Indonesia Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta12780 Indonesia

The rapid development of technology has brought unmanned aerial vehicles (UAVs) to become widely known in the current era. The market of UAVs is also predicted to continue growing with related technologies in the future. UAVs have been used in various sectors, including livestock, forestry, and agriculture. In agricultural applications, UAVs are highly capable of increasing the productivity of the farm and reducing farmers' workload. This paper discusses the application of UAVs in agriculture, particularly in spraying and crop monitoring. This study examines the urgency of UAV implementation in the agriculture sector. A short history of UAVs is provided in this paper to portray the development of UAVs from time to time. The classification of UAVs is also discussed to differentiate various types of UAVs. The application of UAVs in spraying and crop monitoring is based on the previous studies that have been done by many scientific groups and researchers who are working closely to propose solutions for agriculture-related issues. Furthermore, the limitations of UAV applications are also identified. The challenges in implementing agricultural UAVs in Indonesia are also presented. Copyright © 2023, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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AI-based models to predict decompensation on traumatic brain injury patients

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computers in Biology and Medicine 2025年 186卷 109634-109634页

作者： Ribeiro, Ricardo Neves, Inês Oliveira, Hélder P. Pereira, Tania INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal FCUP - Faculty of Sciences of the University of Porto Porto Portugal ULS - Unidade Local de Saúde São José Lisbon Portugal FCTUC - Faculty of Sciences and Technology of the University of Coimbra Coimbra Portugal

Traumatic Brain Injury (TBI) is a form of brain injury caused by external forces, resulting in temporary or permanent impairment of brain function. Despite advancements in healthcare, TBI mortality rates can reach 30%–40% in severe cases. This study aims to assist clinical decision-making and enhance patient care for TBI-related complications by employing Artificial Intelligence (AI) methods and data-driven approaches to predict decompensation. This study uses learning models based on sequential data from Electronic Health Records (EHR). Decompensation prediction was performed based on 24-h in-mortality prediction at each hour of the patient's stay in the Intensive Care Unit (ICU). A cohort of 2261 TBI patients was selected from the MIMIC-III dataset based on age and ICD-9 disease codes. Logistic Regressor (LR), Long-short term memory (LSTM), and Transformers architectures were used. Two sets of features were also explored combined with missing data strategies by imputing the normal value, data imbalance techniques with class weights, and oversampling. The best performance results were obtained using LSTMs with the original features with no unbalancing techniques and with the added features and class weight technique, with AUROC scores of 0.918 and 0.929, respectively. For this study, using EHR time series data with LSTM proved viable in predicting patient decompensation, providing a helpful indicator of the need for clinical interventions. © 2025 Elsevier Ltd

关键词： Electronic health record

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Binocular Disparity Unveils the Mechanisms of Stereo Feature Selectivity: Orientation and Motion

Binocular Disparity Unveils the Mechanisms of Stereo Feature...

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International Joint Conference on Neural Networks (IJCNN)

作者： Bin Li Yuki Todo Zheng Tang Cheng Tang Division of Electrical Engineering and Computer Science Kanazawa University Kanazawa Japan Division of Electrical Information and Communication Engineering Kanazawa University Kanazawa Japan Department of Intelligence Information Systems University of Toyama Toyama Japan Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Binocular vision serves as the foundation for stereo vision, providing humans with the ability to perceive the three-dimensional information of their surroundings. However, although some cortical neurons are reported with 3D information selectivity, the interconnections between binocular input and cortical neuron firing have yet to be fully established, impeding progress in our understanding of stereo information perception. In this work, we aim to address this issue by examining the causality between retinal input and selective neuron firing. We propose a general mechanism of stereo orientation and motion direction detection based solely on binocular disparity input, which is the difference in visual information between the two eyes. Our results suggest that this disparity-based mechanism is robust and can effectively complete stereo orientation and motion direction detection. Our proposed general perceiving mechanism has the potential to contribute to the resolution of the complex problem of binocular information processing and computation. Further research into this area may help to deepen our understanding of stereo vision and provide insights into the underlying neural mechanisms.

关键词： Visualization Three-dimensional displays Firing Neurons Noise Reliability theory Retina

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Joint Message Detection, Channel, and User Position Estimation for Unsourced Random Access in Cell-Free Networks

Joint Message Detection, Channel, and User Position Estimati...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Eleni Gkiouzepi Burak Çakmak Manfred Opper Giuseppe Caire Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany Institute of Mathematics University of Potsdam Germany Centre for Systems Modeling and Quantitative Biomedicine University of Birmingham United Kingdom

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

We consider unsourced random access (uRA) in user-centric cell-free (CF) wireless networks, where random access users send codewords from a common codebook during specifically dedicated random access channel (RACH) slots. The system is conceptually similar to the so-called 2-step RACH currently discussed in 3GPP standardization. In order to cope with the distributed and CF nature of the network, we propose to partition the network coverage area into zones (referred to as "locations") and assign an uRA codebook to each location, such that users in a certain location make use of the associated codebook. The centralized uRA decoder makes use of the multisource AMP algorithm recently proposed by the authors. This yields at once the list of active uRA codewords, an estimate of the corresponding channel vectors, and an estimate of the active users’ position. We show excellent performance of this approach and perfect agreement with the rigorous theoretical "state evolution" analysis. We also show that the proposed "location-based" partitioned codebook approach significantly outperforms a baseline system with a single non-partitioned uRA codebook.

关键词： Wireless networks Message passing Channel estimation Signal processing algorithms Estimation Standardization Signal processing Vectors Partitioning algorithms Decoding

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An Artificial Neural Network for Image Classification Inspired by Aversive Olfactory Learning Circuits in Caenorhabditis Elegans

arXiv

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arXiv 2024年

作者： Wang, Xuebin Liu, Chunxiuzi Zhao, Meng Zhang, Ke Di, Zengru Liu, He Department of Systems Science Faculty of Arts and Sciences Beijing Normal University Zhuhai519087 China International Academic Center of Complex Systems Beijing Normal University Zhuhai519087 China School of Systems Science Beijing Normal University Beijing100875 China School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

This study introduces an artificial neural network (ANN) for image classification task, inspired by the aversive olfactory learning circuits of the nematode Caenorhabditis elegans (C. elegans). Despite the remarkable performance of ANNs in a variety of tasks, they face challenges such as excessive parameterization, high training costs and limited generalization capabilities. C. elegans, with its simple nervous system comprising only 302 neurons, serves as a paradigm in neurobiological research and is capable of complex behaviors including learning. This research identifies key neural circuits associated with aversive olfactory learning in C. elegans through behavioral experiments and high-throughput gene sequencing, translating them into an image classification ANN architecture. Additionally, two other image classification ANNs with distinct architectures were constructed for comparative performance analysis to highlight the advantages of bio-inspired design. The results indicate that the ANN inspired by the aversive olfactory learning circuits of C. elegans achieves higher accuracy, better consistency and faster convergence rates in image classification task, especially when tackling more complex classification challenges. This study not only showcases the potential of bio-inspired design in enhancing ANN capabilities but also provides a novel perspective and methodology for future ANN design. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Fast Swarming of UAVs in GNSS-denied Feature-poor Environments without Explicit Communication

arXiv

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arXiv 2024年

作者： Horyna, Jiří Krátký, Vít Pritzl, Václav Báča, Tomáš Ferrante, Eliseo Saska, Martin Multi-Robot Systems Group Faculty of Electrical Engineering Czech Technical University in Prague Technicka 2 Prague Czech Republic Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands

A decentralized swarm approach for the fast cooperative flight of Unmanned Aerial Vehicles (UAVs) in feature-poor environments without any external localization and communication is introduced in this paper. A novel model of a UAV neighborhood is proposed to achieve robust onboard mutual perception and flocking state feedback control, which is designed to decrease the inter-agent oscillations common in standard reactive swarm models employed in fast collective motion. The novel swarming methodology is supplemented with an enhanced Multi-Robot State Estimation (MRSE) strategy to increase the reliability of the purely onboard localization, which may be unreliable in real environments. Although MRSE and the neighborhood model may rely on information exchange between agents, we introduce a communication-less version of the swarming framework based on estimating communicated states to decrease dependence on the often unreliable communication networks of large swarms. The proposed solution has been verified by a set of complex real-world experiments to demonstrate its overall capability in different conditions, including a UAV interception-motivated task with a group velocity reaching the physical limits of the individual hardware platforms. Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

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Adaptive RBF-Neural-Network Control with Force Observer for Teleoperation Robotic System 1st

Adaptive RBF-Neural-Network Control with Force Observer for...

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1st International Conference on Cognitive Computation and systems, ICCCS 2022

作者： An, Zhaohui Min, Gaochen Jiang, Xiangzuo Yu, Xinbo He, Wei Silvestre, Carlos School of Intelligence Science and Technology Institute of Artificial Intelligence University of Science and Technology Beijing Beijing100083 China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing100083 China Donald Bren School of Information and Computer Sciences University of California IrvineCA92697 United States Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China LARSyS Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

ISBN: (纸本)9789819927883

In this paper, an adaptive radial basis function neural network (RBFNN) control strategy is proposed for bilateral teleoperation robotic system with uncertainty and time delay. Meantime, the force observer method is used to estimate the interaction force between the slave robot and environment. The model of teleoperation robotic system is analyzed, and then the uncertainty of bilateral teleoperation robotic system is estimated by using RBFNN. Using Lyapunov stability theorem, the stability of teleoperation robotic system under our proposed control is proved. Finally, the effectiveness of the proposed control algorithm is verified by Matlab Simulink, and position tracking and force estimation performance of the teleoperation robotic system are guaranteed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time delay

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FPGA Design and Implementation of Driving Lane Detection on Zynq-7000 SoC 44

FPGA Design and Implementation of Driving Lane Detection on ...

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44th International Convention on Information, Communication and Electronic Technology, MIPRO 2021

作者： Martin, M. Grbic, R. Subotic, M. Kastelan, I. RT-RK Institute for Computer Based Systems Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia University of Novi Sad Faculty of Technical Sciences Novi Sad Serbia

ISBN: (纸本)9789532331011

Number of different Advanced Driving Assistant systems (ADAS) are implemented in modern vehicles. One of the important ADAS is lane departure warning system which warns driver when lane switching is occurring unintentionally. Integral part of such system is a lane detection algorithm based on front-view camera images. In this paper, an algorithm for detecting lane markings from images is designed and implemented in FPGA technology on Zynq-7000 System-on-Chip (SoC) using VHDL. The algorithm is based on traditional computer vision techniques to obtain lane markings and detect driving lane. The algorithm evaluation was performed on images of different resolution in various conditions. The high performance has been achieved in terms of lane detection, but improvements are needed in terms of algorithm execution speed. © 2021 Croatian Society MIPRO.

关键词： Field programmable gate arrays (FPGA)

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