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SSIG: Single-Sample Information Gain Model for Integrating Multi-Omics Data to Identify Cancer Subtypes

Chinese Journal of Electronics 2021年第2期30卷 303-312页

作者： ZHANG Yuanyuan WANG Ziqi WANG Shudong KOU Chuanhua School of Information and Control Engineering Qingdao University of Technology College of Computer Science and Technology China University of Petroleum (East China)

Different living environments of cancer samples lead to different molecular mechanisms of cancer development, which in turn leads to different cancer subtypes. How to identify cancer subtypes is a key issue for the realization of precision medicine. With the development of high-throughput technologies, multi-omics data which can better understand different causes of cancer have emerged. However, the current methods of analyzing cancer subtypes using multi-omics data is mostly derived from population cancer sample data and ignores the differences between different cancer ***, the joint analysis of multi-omics based on a single sample may reveal more information about the differences between individual cancers. A strategy for identifying cancer subtypes is proposed based on Single-sample information gain(SSIG) which construct sample feature matrix by considering the heterogeneity of sample. Applying this strategy to current popular subtype identification methods, cancer subtypes can be identified more accurately and the mechanism of cancer can be found from the perspective of a single sample. By comparing different methods in different clustering measure, and using survival analysis, it is shown that SSIG is more suitable for cancer subtype identification than the original multi-omics data, and it is easier to mine the cancer subtype classification mechanism hidden behind the data.

关键词： identify cancer subtypes SSIG medical computing cancer subtype classification mechanism Single-sample information gain model different cancer subtypes original multiomics data genetics cancer subtype identification current popular subtype identification methods population cancer sample data different cancer samples data analysis cancer development genomics cancer sample feature matrix different molecular mechanisms different living environments bioinformatics individual cancers different clustering measure

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Analysis of YouTube Video Comments with NLP Methods 16

Analysis of YouTube Video Comments with NLP Methods

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Mironela, Pirnau Iustin, Priescu Mihaela, Priescu Catalina Claudiu, Pirnau Daniel, Ghiculescu Liviu 'Titu Maiorescu' University Faculty of Informatics Department of Informatics Bucharest Romania National University of Science and Technology Politehnica Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania National University of Science and Technology Politehnica Bucharest Faculty of Industrial Engineering and Robotics Manufacturing Engineering Department Bucharest Romania

ISBN: (纸本)9798350371154

This study proposes methods that can be used to examine and interpret comments that users have made after watching videos on YouTube on a particular topic. YouTube tutorials are very popular among young people. They have become an important pillar in informal education, thus contributing to the rapid acquisition of skills and knowledge. Under these circumstances, we were interested in analysing the YouTube videos comments, knowing that the platform has a continuous increase in popularity, which is also due to the opportunities of sharing them. Since ChatGPT-themed YouTube videos have seen a significant surge in popularity since 2022, we were interested to analyse some videos that approach the topics of 'ChatGPT, AI clone, AI robot and Deep Learning.' © 2024 IEEE.

关键词： Deep learning

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Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation

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computers, Materials & Continua 2022年第8期72卷 2467-2482页

作者： Marwa M.Eid Fawaz Alassery Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Shady Y.El-Mashad Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Mechanical Engineering Department Qassim UniversityBuraidah51452Saudi Arabia Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt

The sample’s hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing ***(HGB)is a critical component of the human body because it transports oxygen from the lungs to the body’s tissues and returns carbon dioxide from the tissues to the *** the HGB level is a critical step in any blood analysis *** often indicate whether a person is anemic or polycythemia *** ensemble models by combining two or more base machine learning(ML)models can help create a more improved *** purpose of this work is to present a weighted average ensemble model for predicting hemoglobin *** optimization method is utilized to get the ensemble’s optimum *** optimum weight for this work is determined using a sine cosine algorithm based on stochastic fractal search(SCSFS).The proposed SCSFS ensemble is compared toDecision Tree,Multilayer perceptron(MLP),Support Vector Regression(SVR)and Random Forest Regressors as model-based approaches and the average ensemble *** SCSFS results indicate that the proposed model outperforms existing models and provides an almost accurate hemoglobin estimate.

关键词： Sine cosine optimization metaheuristics optimization hemoglobin estimation weight average ensemble

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Distributed generalized Nash equilibrium seeking:event-triggered coding-decoding-based secure communication

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science China(Information sciences) 2024年第7期67卷 271-284页

作者： Shaofu YANG Wenying XU Wangli HE Jinde CAO School of Computer Science and Engineering Southeast University School of Mathematics Southeast University Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology

In this paper, we consider the distributed generalized Nash equilibrium(GNE) seeking problem in strongly monotone games. The transmission among players is implemented through a digital communication network with limited bandwidth. For improving communication efficiency or/and security, an event-triggered coding-decoding-based communication is first proposed, where the data(decision variable) are first mapped to a series of finite-level codewords and, only when an event condition is satisfied, then sent to the neighboring agents. Moreover, a distributed communication-efficient GNE seeking algorithm is constructed accordingly,and the overrelaxation scheme is further taken into consideration. Through primal-dual analysis, the proposed algorithm is proven to converge to a variational GNE with fixed step-sizes by recasting it as an inexact forward-backward iteration. Finally, numerical simulations illustrate the benefit of the proposed algorithms in terms of saving communication resources.

关键词： noncooperative games event-triggered communication coding-decoding quantization

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Short Circuit and Connection Fault Diagnosis for Lithium-ion Battery Pack Based on Differential Voltage Changes and Independent Component Analysis 4

Short Circuit and Connection Fault Diagnosis for Lithium-ion...

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4th International Conference on Energy, Power and Electrical engineering, EPEE 2024

作者： Zhang, Luan Liu, Hanxiao Li, Yang Li, Liwei Shandong University School of Control Science and Engineering Jinan China Qingdao Power Supply Company Shandong Electric Power Company State Grid Qingdao China Shandong University School of Computer Science and Technology Qingdao China

ISBN: (纸本)9798331518066

In order to avoid thermal runaway as well as ensure safe and stable operation of battery system, it is vital to perform rapid and accurate diagnosis for early faults with similar characteristics. In this paper, a short circuit and connection fault diagnosis method that is applicable to traditional one-to-one voltage sensor topology is proposed based on differential voltage changes (DVCs) and independent component analysis. Voltage measurements is pretreated to obtain DVCs for extracting fault signature and reducing the detrimental effects of inconsistency, interference, etc. to a certain extent. Independent component analysis is combined with the DVC vector to establish diagnostic model and diagnose faults online. Fault detection and isolation is achieved using squared prediction error, and considering that traditional contributions cannot accurately determine fault location, an improved contribution calculation is introduced to localize the faulty cell. Experimental verifications show that the proposed method can timely detect and localize faults, meanwhile it can distinguish short circuit with connection fault. © 2024 IEEE.

关键词： Lithium-ion batteries

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Named Entity Recognition for Safety Risks in Electrical Work

Named Entity Recognition for Safety Risks in Electrical Work

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Liu, Peng Fan, Shanshan Cao, Xiaofan Jia, Yujiao Yang, Jianing Institute of Energy and Electrical Engineering Changchun University of Science and Technology Changchun China School of Control and Computer Engineering North China Electric Power University Beijing China School of Electrical Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798350378214

The texts of safety risks in eletrical work describe the safety risks involved and management measures. The text records information on potential risks, risk levels, and preventive measures within the electrical work, which is crucial for extracting and mining safety risks. Named Entity Recognition for Safety Risks in Electrical Work (EWSR-NER) is a key technique for information extraction and knowledge discovery. It identifies objects related to safety risks in electrical work, such as risk level, risk factors, risk categories and protective measures, which convey critical meanings within the text. Current Named Entity Recognition (NER) is used to identify generic entities. However, their accuracy is limited by inherent linguistic irregularities in natural language texts, such as nested entities, lengthy entities, and the scarcity of domain-specific annotated corpora. To address this issue, the established standards and principles of safety risk texts in electrical work were followed to analyze the structure and characteristics of the text, developing a dataset of safety risk in electrical work. A new model, RoBERTa-BiLSTM-Efficient GlobalPointer (RoBEGP), was proposed in this paper. It uses the Robertly Optimized Bidirectional Encoder Representation from Transformers Pretraining Approach(RoBERTa) pretraining model to learn the characteristics of electrical work content and generate representation vectors. These representation vectors are input into a dynamic fusion layer, which adjusts the weights of each layer's representation vectors based on the specific requirements of the task, reducing the dimensionality from 1024 to 512. The fused vectors are input into a Bidirectional Long Short-Term Memory(BiLSTM) network to generate feature representations. Efficient GlobalPointer decodes these representations to identify nested entities within the text. Experimental results show that the proposed model achieves an F1 score of 92.38% on a self-constructed dataset of safe

关键词： Risk assessment

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Spatial data intelligence and city metaverse: A review

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Fundamental Research 2024年第3期5卷 1169-1193页

作者： Meng, Xiaofeng Li, Yong Liu, Ke Liu, Yu Yang, Bin Song, Xuan Liao, Guoqiong Wang, Senzhang Yu, Ziqiang Chen, Longbiao Pan, Xiao Lin, Yuming School of Information Renmin University of China Beijing 100872 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Department of Information Sciences National Natural Science Foundation of China Beijing 100085 China Institute of Remote Sensing and Geographic Information Systems School of Earth and Space Sciences Peking University Beijing 100871 China School of Data Science and Engineering East China Normal University Shanghai 200062 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen 518055 China School of Information Management Jiangxi University of Finance and Economics Nanchang 330013 China School of Computer Science and Engineering Central South University Changsha 410017 China School of Computer and Control Engineering Yantai University Yantai 264005 China Department of Computer Science Xiamen University Xiamen 361000 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang 050043 China

Spatial Data Intelligence (SDI) encompasses acquiring, storing, analyzing, mining, and visualizing spatial data to gain insights into the physical world and uncover valuable knowledge. These understandings and knowledge play a crucial role in connecting physical and virtual realms, such as in developing a City Metaverse (CM) aimed at enhancing and optimizing modern urban environments. The advancement of CM holds immense potential to benefit urban dwellers, making research on SDI an increasingly prominent area of focus. This paper contributes significantly by organizing the relevant research and technologies within a coherent framework. Firstly, we identify SDI technologies capable of collecting real-world information to construct a virtual CM. Subsequently, we delve into the technologies that can be compositely integrated with SDI to facilitate interaction with and management of actual cities from the virtual perspective. Additionally, we emphasize the effectiveness and potential of these methods in practical applications. Lastly, we conclude our survey by discussing emerging challenges associated with technological progress, the industrial chain, legal and regulatory aspects, and ethical and moral considerations. © 2024

关键词： Application prospect City metaverse Smart city Spatial data intelligence Virtual-real interaction

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Research on SDN traffic anomaly detection technology based on knowledge graph 5

Research on SDN traffic anomaly detection technology based o...

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5th International Conference on Artificial Intelligence and computer science, AICS 2023

作者： Li, Suyang Bai, Xiaojun Wang, Shenhang School of Computer Science and Engineering Xi’an Technological University Shaanxi Xi’an710021 China Beijing Aerospace Automatic Control Institution Beijing100076 China

ISBN: (纸本)9781510668621

SDN (Software Defined Networking) is a novel network architecture that allows for flexible configuration and centralized control of resources. However, it also presents new fault problems, particularly in high data volumes and complex topologies where traditional anomaly detection algorithms often need to be revised. To address this issue, we propose a knowledge graph-based approach to SDN fault detection. By leveraging the interpretability and expressiveness of knowledge graphs and the SDN controller’s global information, we construct and continuously update a knowledge graph that enables real-time monitoring and analysis of the network state. Finally, we detect and diagnose the abnormal conditions in the network through graph inference. The experimental results indicate that the knowledge graph-based SDN fault detection algorithm demonstrates high accuracy and efficiency, effectively enhancing SDN networks’ operational stability and security. © 2023 SPIE.

关键词： Anomaly detection

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Computational imaging based on single-photon detection: a survey

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Artificial Intelligence Review 2025年第8期58卷 1-54页

作者： Pu, Yanyun Zhu, Chengyuan Yao, Gongxin Li, Chao Pan, Yu Yang, Kaixiang Yang, Qinmin State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Zheda Road Zhejiang Hangzhou310027 China Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Zheda Road Zhejiang Hangzhou310027 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China

The rapid advancements in single-photon detectors with picosecond timing resolution over the past decade have significantly driven the development of time-correlated single-photon counting (TCSPC) for computational imaging applications, including bioimaging and remote sensing. In this review, we utilize the CiteSpace tool to create knowledge maps and perform a bibliometric analysis of this research area. Furthermore, we provide a comprehensive overview of the key challenges associated with computational imaging using temporal single-photon counting. We also highlight how these challenges have been addressed under extreme conditions to establish a reference model for future imaging solutions. We examine the performance evaluation parameters of single-photon detectors to enhance the understanding of detector array scaling and their application in constructing efficient computational imaging systems. Lastly, we aim to elucidate the current technical challenges in single-photon detector-based computational imaging and explore their potential future developments. © The Author(s) 2025.

关键词： Computational Imaging

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN 2

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Krishna, Tiruveedula Gopi Sreenivas, Nune Ajay Kumar, M. Reddy, Chandra Sekhar Rathee, Davinder Sing Urgessa, Teklu College of Electrical Engineering and Computing Adama Science and Technology University Department of Computer Science and Engineering Adama Ethiopia Andhra Loyala institute of Engineering and Technology Department of Electrical and Electronics Engineering Vijayawada India College of Electrical Engineering and Computing Adama Science and Technology University Department of Electrical Power and Control Engineering Adama Ethiopia College of Electrical Engineering and Computing Adama Science and Technology University Department of ECE Adama Ethiopia

ISBN: (纸本)9798331505745

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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