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38th CCF National Conference of Computer applications, CCF NCCA 2023

作者： Zhang, Zhao Shen, Xiaobin Ge, Jing Zha, Yingying Liu, Lisai Liu, Sheng College of Computer Science and Technology Huaibei Normal University Huaibei235000 China Anui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei235000 China

ISBN: (纸本)9789819987634

To assist blind people in travelling and solve the problems of high hardware costs, insufficient portability of blind assistance devices, and vulnerability to environmental impacts, a modified YOLOv5 algorithm (YOLOv5-CM) based on ground plane segmentation and Euclidean clustering algorithms is proposed and applied to a blind guidance system based on RealSense D435. This proposed algorithm adds coordinate attention and uses MobileNetv3 as the backbone network for YOLOv5s to extract the main features. Compared with those of the YOLOv5 model, the mAP was improved by 0.7%, the model size was decreased by 79.1%, and the number of parameters was reduced by 80.3%. In this study, the YOLOv5-CM algorithm was applied to design a blind guidance system. When the system detects a traffic light, the RealSense D435 is used to obtain depth images to detect and measure the distance from the pedestrian traffic light. The system provides warnings based on the measured distance. When the system does not detect a traffic light, it utilizes the ground plane segmentation and Euclidean clustering algorithms to obtain the location and distance of obstacles. This information is conveyed to the blind through voice guidance, which aids navigation. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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Prospects and Challenges in COVID-19 Study: A Review Based on Influencing Factors and Analysis Methods 23

Prospects and Challenges in COVID-19 Study: A Review Based o...

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6th International Conference on Big Data Technologies, ICBDT 2023

作者： Liu, Lisai Zhu, Dashuang Zheng, Tianlong Niu, Yiheng Zhang, Zhao Zhang, Zihan Zhu, Xu Liu, Sheng College of Computer Science and Technology Huaibei Normal University Huaibei235000 China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei235000 China

ISBN: (纸本)9798400707667

The unprecedented Coronavirus Disease 2019 had spread and diffused in many countries and regions worldwide, posing a serious threat to people's health and lives. This review presents a systematic analysis and summary of representative research on the role of AI technology in the prediction and containment of COVID-19 and influencing factors of its transmission. To further explore the influences of multiple factors on the incidence and transmission of COVID-19, we used total cases per million (TCPM) and total deaths per million (TDPM) as dependent variables and influencing factors as explanatory variables to conduct multiple linear regression, and constructed four training models: xgboost model (XGBR), random forest regenerator (RFR), extra trees regenerator (ETR), and gradient boosting regenerator (GBR) to predict the test data. This study highlights the contribution and application prospects of AI related technologies in the study of the spread of COVID-19, which will help deepen the global understanding of the COVID-19 pandemic, and provide a reference for curbing the wanton spread of COVID-19 and other diseases again. © 2023 ACM.

关键词： COVID-19

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Knowledge Atlas on the Medical Artificial Intelligence research-A Bibliometric Analysis Based on CiteSpace and VOSviewer 2

Knowledge Atlas on the Medical Artificial Intelligence Resea...

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2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum, AIBDF 2022

作者： Shi, Li Han, Lingling Huang, Ke Ge, Fangzhen Zhang, Lele School of Computer Science and Technology Huaibei Normal University Huaibei235000 China School of Economics and Management Huaibei Normal University Huaibei235000 China Engineering Research Center of Cognitive Behavioral Intelligent Computing and Application Huaibei235000 China

ISBN: (纸本)9781510663107

In order to draw and analyze the current status of the research on the application of artificial intelligence in the field of health in China and abroad, we explored the hotspots and frontiers of this research field and the evolution path from 2012 to 2022. This research uses 3743 articles included in the core database of Web of Science and 728 related research articles contained in CNKI as the basis for data analysis, and uses the advantages of bibliometrics visualization software of CiteSpace and VOSviewer to study institutions, countries, authors and literature keywords. In this research area, the research of medical artificial intelligence is growing day by day. By analyzing and sorting out the current research status, research hotspots and evolution path in this field of research, and trying to point out the deficiencies in the research in this field in China, the paper hopes to provide more references for researchers and practitioners of medical artificial intelligence related research. © 2023 SPIE.

关键词： Data visualization

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MCT-Net: a multi-branch hybrid CNN-transformer model for medical image segmentation

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Pattern Analysis and applications 2025年第2期28卷 1-21页

作者： Shen, Longfeng Diao, Liangjin Peng, Rui Chen, Jiacong Lu, Zhengtian Ge, Fangzhen Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB) College of Computer Science and Technology Huaibei Normal University Anhui Huaibei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Big-Data Research Center on University Management Huaibei Normal University Anhui Huaibei China

In recent years, neural networks have demonstrated substantial progress in medical image segmentation. However, accurately segmenting objects in medical images is often restricted by edge blurring, which complicates the delineation of structures of interest. The purpose of this study is to develop a multi-branch hybrid CNN-transformer model to address the issue of edge blurring in medical image segmentation. This approach enables more precise edge detection and segmentation. Integrating hybrid CNN and transformer decoders facilitates detailed recovery while preserving global structures, thus minimizing errors in regions with blurred edges. Additionally, we incorporate a hybrid attention mechanism at the model’s bottleneck to enhance feature transfer between the encoder and decoder. The model was trained using the ACDC, Synapse, and polyp datasets, with a focus on improving segmentation accuracy in regions with blurry edges. Experimental results reveal that the proposed model surpasses existing state-of-the-art methods on the ACDC, Synapse, and polyp datasets, demonstrating its superior performance and effectiveness. These results suggest that the proposed model offers a significant improvement in handling edge blurring, with potential applications in enhancing medical image analysis and diagnostics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Attention mechanism Convolutional neural network Edge information Medical image segmentation Transformer

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A portable,low-cost lactate measurement system

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Journal of Measurement Science and Instrumentation 2025年第1期16卷 37-46页

作者： WANG Qingqing PAN Yu YU Chuanxin WANG Yanyan ZHANG Kaikai LIU Sheng School of Computer Science and Technology Huaibei Normal UniversityHuaibei 235000China Anhui Provincial Engineering Research Center of Cognitive Behavioral Intelligent Computing and Application Huaibei 235000China

Lactate,as a metabolite,plays a significant role in a number of fields,including medical diagnostics,exercise physiology and food *** methods for lactate measurement often involve expensive and cumbersome *** study developed a portable and low-cost lactate measurement system,including independently detectable hardware circuits and user-friendly embedded software,computer,and smartphone *** experiment verified that the relative error of the detection current in the device circuit was less than 1%.The electrochemical performance was measured by comparing the[Fe(CN)_(6)]^(3−)/[Fe(CN)_(6)]^(4−)solution with the desktop electrochemical workstation CHI660E,and a nearly consistent chronoamperometry(CA)curve was *** modified lactate sensors were used for CA testing of *** the concentration range of 0.1 mmol·L^(−1)to 20 mmol·L^(−1),there was a good linear relationship between lactate concentration and steady-state current,with a correlation coefficient(R2)greater than 0.99 and good repeatability,demonstrating the reliability of the developed *** lactate measurement system developed in this study not only provides excellent detection performance and reliability,but also achieves portability and low cost,providing a new solution for lactate measurement.

关键词： lactate measurement portable device embedded development lactate sensor electrochemical analysis

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Medical CT Image Super-resolution Algorithm based on GAN

Medical CT Image Super-resolution Algorithm based on GAN

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Computer, Vision and intelligent Technology (ICCVIT), International Conference on

作者： Saihu Tian Taile Peng Zhongan Wang Zhengfeng Li School of Computer Science and Technology Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei Normal University Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

Computed Tomography (CT) images are essential for visualizing the internal structures of the human body and are widely applied in the diagnosis of various diseases. However, during the processes of generating, storing, and transmitting medical CT images, the clarity of these images can be degraded by non-human factors, resulting in issues such as reduced resolution and blurred texture details, thereby compromising image fidelity. In this study, we present an approach utilizing Generative Adversarial Networks (GANs). that incorporates a grayscale value loss as an additional constraint while maintaining the original loss function constraints. We conduct experiments for 4x super-resolution reconstruction. To evaluate the reconstruction performance, we compare the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index (SSIM) of the reconstructed images. The experimental results demonstrate ,which the proposed method exhibits promising performance in both quantitative metrics and visual quality.

关键词： Visualization PSNR Computed tomography Computational modeling Superresolution Noise Gray-scale Generative adversarial networks Image reconstruction Medical diagnostic imaging

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An Improved Lightweight CNN_BiLSTM Model for Network Intrusion Detection Using the UNSW-NB15 Dataset

An Improved Lightweight CNN_BiLSTM Model for Network Intrusi...

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Computer, Vision and intelligent Technology (ICCVIT), International Conference on

作者： Licheng Zhuang Jun Hu Qingqing Wang Yanyan Wang Kaikai Zhang Sheng Liu School of Computer Science and Technology Huaibei Normal University Huaibei China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

The attack behavior in the network poses a threat to security and privacy issues. In order to improve the effectiveness of IoT network attack detection and compensate for the shortcomings of traditional machine learning methods in detection capabilities, a network intrusion detection method based on lightweight convolutional neural networks (CNN) and bidirectional long short-term memory networks (BiLSTM) is proposed. This method preprocesses data through normalization techniques, extracts data features from both spatial and temporal dimensions using CNN and BiLSTM models, and inputs the extracted spatiotemporal feature vectors into the CNN BiLSTM upper classifier for training, achieving intrusion detection. The proposed model was validated on the dataset UNSW-NB15, and the results showed that the binary and multi classification accuracy of the CNN_BiLSTM model was around 93%. Compared with other methods, the proposed method significantly improves the accuracy of network intrusion detection, and also demonstrates better performance in detection accuracy and false positive rate.

关键词： Training Accuracy Bidirectional long short term memory Network intrusion detection Feature extraction Vectors Spatiotemporal phenomena Convolutional neural networks Data mining Security

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A Conflicting Evidence Fusion Method Based on Game Theory Combination Weighting

A Conflicting Evidence Fusion Method Based on Game Theory Co...

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Computer, Vision and intelligent Technology (ICCVIT), International Conference on

作者： Ziye Wang Jianyu Xiao School of Computer Science and Technology Huaibei Normal University Huaibei China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

This essay introduces a new approach to address the challenges of applying Dempster-Shafer evidence theory to tackling high-conflict evidence. We first employed Jousselme’s distance and compatibility coefficients to measure confidence and support, then converted these values into vectors. Next, we applied game theory to convert the vectors into a system of linear equations,from which we obtained a corresponding combination of weight coefficients that we used to modify the initial evidence. Finally, we applied the Dempster-Shafer combination rule to achieve evidence fusion. A case analysis shows that our proposed method provides more sensible fusion results for high-conflict evidence.

关键词： Computer vision Accuracy Evidence theory Data integration Reliability theory Vectors Game theory Convergence

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A Conflicting Evidence Fusion Method Based on a Greedy Strategy

A Conflicting Evidence Fusion Method Based on a Greedy Strat...

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Computer, Vision and intelligent Technology (ICCVIT), International Conference on

作者： Haibo Li Jianyu Xiao Qi Tang School of Computer Science and Technology Huaibei Normal University Huaibei China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

To address the issue of the conventional D-S combination rule not effectively dealing with highly conflicting evidence fusion, a conflicting evidence fusion algorithm utilizing a greedy strategy is proposed. First, by adopting a greedy strategy, the effective evidence distance is defined based on Jousselme’s evidence distance, followed by the definition of strongly and weakly correlated evidence. The average value of the effective evidence distance is calculated, and based on this the evidence is classified. Finally, weights are calculated individually for the classified evidence. Weights are then reassigned to the two types of evidence, and the corrected evidence is fused to obtain the final fusion result. Numerical examples are provided to demonstrate the effectiveness of the method proposed in this paper in addressing the limitations of the conventional D-S combination rule for fusing highly conflicting evidence.

关键词： Computer vision Correlation Evidence theory Classification algorithms

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research on Kindergarten Drama Education Activity System with Traditional Culture as Carrier under Big Data Platform

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Cheng, Zhihong Cheng, Yu College of Education Huaibei Normal University Anhui Huaibei235000 China Anhui Province Cognitive Behavior Intelligent Computing and Application Engineering Research Center Anhui Huaibei235000 China Faculty of Education Monash University MelbourneVIC3800 Australia

Drama education activities carried out by traditional culture comply with, guide and stimulate children's dramatic nature so that each child can find a suitable place for himself in drama education activities and promote the growth of children with individuality. This paper builds a flipped classroom teaching model for kindergarten drama education activities based on the microblogging public platform. Teachers in the flipped classroom of drama education activities need to collect traditional cultural drama resources before class, and the basic framework of a personalized drama resource recommendation system is proposed on the WeChat public platform. The optimization of drama resource recommendation results is carried out using the preference score of drama resources using the collaborative filtering recommendation algorithm of multi-featured users. The performance test is conducted for the personalized drama resource recommendation system constructed in this paper to provide drama resources for the development of kindergarten drama education activities in the flipped classroom. The results show that when the preference recommendation of multi-featured users' drama resources has a certain number of users, its accuracy is about 4.5% higher than that of the traditional method, the F-score value is about 0.05 higher than that of the traditional recommendation method, and the real-time updating delay of the personalized recommendation system fluctuates between 1.18s~1.65s. The personalized recommendation system can help teachers to obtain the update and download of traditional culture drama resources in time, make a good guarantee of drama teaching resources for kindergartens to carry out drama education activities with traditional culture as the carrier, and cultivate young children's awareness of the protection of traditional culture. © 2023 Zhihong Cheng et al., published by Sciendo.

关键词： Recommender systems

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