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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Zhao, Haiyan Wu, Youteng Lu, Yang College of Computer Science and Technology Inner Mongolia Minzu University Inner Mongolia China

ISBN: (纸本)9798331517021

Skin cancer is the most common type of cancer, and early detection and treatment of skin lesions can greatly improve the cure rate of skin cancer. However, existing skin disease assisted classification networks have some problems such as large number of parameters and complex operations, which are not convenient for deployment on mobile devices. This paper proposes a lightweight convolution neural network method for skin lesion classification. The method replaces the standard convolution module in the first stage of the network based on the lightweight EfficientN etBO with the Ghost module, and adds the CBAM module between the 1 *1 convolution layer and the pooling layer in the ninth stage, to form an improved EfficientNetBO network model. The accuracy of the improved EfficientN etBO on the ISIC2018 skin disease dataset HAMI0000 is 89.38%, the Macro_P, Macro_R, Fl-Score is 0.88, 0.86, 0.87 respectively. The number of parameters is only 5.19M, while the single inference time is 71ms. All of these indicators make the model suitable for deploying skin disease classification models on mobile devices to assist clinical diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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A Channel-Attention Convolutional Neural Network for Protein-DNA Binding Residue Prediction

A Channel-Attention Convolutional Neural Network for Protein...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Song, Jiazhi Song, Chuyi Zhang, Chenrui Jiang, Jingqing College of Computer Science and Technology Inner Mongolia Minzu University Inner Mongolia China College of Mathematics Science Inner Mongolia Minzu University Inner Mongolia China

ISBN: (纸本)9798331517021

Accurately identifying protein-DNA binding residues is important for protein function annotation and drug design. Previous studies have used classic machine learning algorithms like support vector machines (SVMs) and random forest (RF) to predict protein-DNA binding residues. However, as new machine learning techniques are being developed, the prediction performance could be further improved. In this paper, a convolutional neural network (CNN) based framework is proposed for protein-DNA binding residues prediction, named Se-Residual-Inception network, which includes stacked Inception blocks with short-cut connection and channel-attention mechanism. We examine the performance of proposed Se-Residual-Inception network on the benchmark dataset and independent testing set, and achieve the area under the ROC curve (AUC) values of 0.882 and 0.848, respectively. The prediction performance indicates that our proposed method can precisely identify the DNA binding residues in protein sequences, which can provide assistive directions for wet-lab experiments. © 2024 IEEE.

关键词： Convolutional neural networks

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Introduction

Signals and Communication Technology

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Signals and Communication technology 2024年 Part F1492卷 3-17页

作者： Tian, Yu-Chu Gao, Jing School of Computer Science Queensland University of Technology BrisbaneQLD Australia College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China

The main theme of this book is network analysis and architecture design for network planning. While there are various types of networks, this book specifically focuses on computer networks for data communication. Therefore, it uses the term networks to specifically refer to computer networks unless specified explicitly otherwise. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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An Improved Beluga Whale Optimization Algorithm Based on Chaotic Opposition-Based Learning and Adaptive Weighting Factor

An Improved Beluga Whale Optimization Algorithm Based on Cha...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Zhang, Bingbing Jiang, Jingqing Song, Jiazhi College of Mathematics Science Inner Mongolia Minzu University Inner Mongolia China College of Computer Science and Technology Inner Mongolia Minzu University Inner Mongolia China

ISBN: (纸本)9798331517021

Beluga Whale Optimization (BWO) algorithm faces challenges such as slower convergence, lower accuracy, and instability for dealing with unimodal functions. To tackle these problems, we propose an enhanced version of Beluga Whale Optimization Algorithm (ABWO). ABWO algorithm incorporates chaotic Opposition-Based Learning (OBL) and adaptive weighting factor. We fuse Logistic and Tent chaotic mappings with OBL to ensure an even distribution of individuals in the initial solution space during initialization. Consequently, the population diversity is increased and the quality of the initial solution is better. The adaptive weighting factor $F$ is introduced in the exploitation phase. It increases and then decreases with each iteration. This adjustment enhances the guiding effect of the global optimal position on individual position updates in the early stage of BWO. It also enables the algorithm to execute more effective global searches in the middle stage. Meanwhile, the algorithm converges more quickly in the late stage. Overall, the solution accuracy and convergence stability of the algorithm are improved. On nine unimodal test functions, ABWO has better optimization and convergence performance compared with six basic algorithms. It also outperforms three improved beluga whale optimization algorithms proposed by other scholars. © 2024 IEEE.

关键词： Optimization algorithms

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A Hybrid Particle Swarm Optimization K-Means Algorithm with Quantum Behavior

A Hybrid Particle Swarm Optimization K-Means Algorithm with ...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Liu, Man Yun, Haojie Zhu, Qiyao Chun, Hua College of Computer Science and Technology Inner Mongolia Minzu University Tongliao China

ISBN: (纸本)9798331517021

The K-means algorithm is a partition-based clustering approach with two notable drawbacks, sensitivity to the initial center and a tendency to converge to suboptimal solutions. To address these limitations, a hybrid particle swarm optimization k-means algorithm with quantum be-havior (HQPSO-K) is proposed. The improved algorithm uses a nonlinear contraction-expansion coefficient to balance local optimization and global search. Moreover, Levy flight strategy is incorporated to further improve the particles' capability to evade local optima. Finally, the improved QPSO algorithm combined with K-means explores the search space more efficiently. The algorithm is tested with UCI data set. Experimental results demonstrate that HQPSO-K achieves superior clustering outcomes and is less sensitive to initial conditions. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Big Data Optimization Clustering Algorithm for Power Grid CPS Based on PSO 2

Big Data Optimization Clustering Algorithm for Power Grid CP...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Yue, Junying Arimuzha Inner Mongolia Normal University College Of Computer Science And Technology Hohhot China

ISBN: (纸本)9798350381665

As power systems become increasingly intelligent and internet of things technology finds widespread use, the power grid cyber-physical system (CPS) has become pivotal in real-time monitoring, data processing, and intelligent control. Nonetheless, the generation of large-scale, high-dimensional real-time data within the power grid CPS system poses challenges to traditional data processing and analysis methods. In order to meet this challenge, this paper proposes a PSO-based power grid CPS big data optimization clustering algorithm. By introducing PSO algorithm, the algorithm makes full use of its global search and adaptive characteristics to cluster the massive data in the CPS system of power grid efficiently. Compared with the traditional clustering algorithm, the algorithm proposed in this paper is superior in mining data relations and finding abnormal situations. In the experimental verification stage, we use different scale and complexity power grid data sets to verify the feasibility and performance of the algorithm. The experimental results show that the power grid CPS big data optimization clustering algorithm based on PSO has achieved significant performance improvement in multiple data scenarios. The research results of this paper have important theoretical and practical significance for intelligent management, data mining and anomaly detection of power grid system. © 2024 IEEE.

关键词： Clustering algorithms

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Cloud Computing Resource Load Prediction Based on the Improved Particle Swarm Algorithm Optimizing GRU-RNN Model 20

Cloud Computing Resource Load Prediction Based on the Improv...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Guo, Xiang Hu, Yinggang Song, Chuyi Jiang, Jingqing Song, Jiazhi College of Mathematics Science Inner Mongolia Minzu University Tongliao China College of Computer Science and Technology Inner Mongolia Minzu University Tongliao China

ISBN: (纸本)9798350356328

Consider the inherent shortcomings of particle swarm optimization algorithm(PSO), this paper improves the inertia weight of the particle swarm optimization algorithm. We call this improved algorithm as IPSO. Then, IPSO is used to optimize the number of neurons in each layer of the GRU-RNN model. An IPSO-GRU-RNN prediction model based on improved particle swarm optimization is established to predict the resource load of cloud computing center. The experiment results show that the improved particle swarm optimization algorithm effectively optimizes the model parameters and improves prediction accuracy. © 2024 IEEE.

关键词： Prediction models

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Optimization of Survey Lines Under Multibeam Bathymetric Systems Based on Iterative Analysis 4

Optimization of Survey Lines Under Multibeam Bathymetric Sys...

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4th International Conference on computer science and Blockchain, CCSB 2024

作者： Yu, Kexing Liu, Chang Zhai, Jiale Zhou, Yanbin College of Computer Science and Technology Inner Mongolia Normal University Inner Mongolia Hohhot011517 China College of Mathematics Science Inner Mongolia Normal University Inner Mongolia Hohhot011517 China

ISBN: (纸本)9798350356502

In this paper, we explore the optimization of survey lines in multibeam bathymetric systems through an iterative analysis approach. Recognizing the challenges of survey line configuration, coverage efficiency, and data redundancy, we propose mathematical models aimed at dynamically adjusting survey lines to meet specific environmental constraints and survey objectives. Our methodology includes the modeling of overlap rates and coverage widths, considering factors such as seafloor topography and survey vessel capabilities. The results demonstrate that our approach significantly enhances the accuracy and efficiency of seabed mapping, offering practical insights for marine surveyors. This research contributes to the field of marine geodesy by providing a foundation for further advancements in multibeam bathymetric surveying and highlights the potential of iterative analysis in improving marine exploration and preservation efforts. © 2024 IEEE.

关键词： Bathymetry

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A Reduced State Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri Nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model checking method traverses the full state space, which suffers from the state space explosion problem. A reduced state space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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Early Screening Prediction System of Breast Cancer MRI Images Based on Deep Learning 3

Early Screening Prediction System of Breast Cancer MRI Image...

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3rd IEEE International Conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Xu, Li Liu, Liang Baotou Medical College Inner Mongolia University of Science and Technology Department of Computer Science and Technology Baotou014040 China

ISBN: (纸本)9798350380989

With the increasing incidence rate of breast cancer in China, how to effectively improve the accuracy and sensitivity of breast cancer diagnosis has become an important issue facing the Chinese medical industry. In recent years, the application of computer deep learning technology in medical images has become increasingly widespread. It can automatically extract features from images and classify and predict them. At present, deep learning has made significant breakthroughs in the field of medical image analysis. Deep learning algorithms can automatically discover disease features in massive image data, and their performance has also been significantly improved. The purpose of this study was to study and analyze breast cancer MRI (Magnetic Resonance Imaging) images by using deep learning algorithm, hoping to provide help for early diagnosis of breast cancer. The results showed that the accuracy of the early screening prediction system for breast cancer MRI images based on deep learning reached 95.6%, which laid the foundation for the early diagnosis and treatment of breast cancer. © 2024 IEEE.

关键词： Breast Cancer Prediction Deep Learning Early Screening Prediction System Magnetic Resonance Imaging

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