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An Adaptive Lasso Grey Model for Regional FDI Statistics Prediction

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Computers, Materials & Continua 2021年第11期69卷 2111-2121页

作者： Juan Huang Bifang Zhou Huajun Huang Jianjiang Liu Neal N.Xiong Centre for Innovation Research in Social Governance Changsha University of Science and TechnologyChangsha410114China College of Information Technology and Management Hunan University of Finance and EconomicsChangsha410205China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464USA

To overcome the deficiency of traditional mathematical statistics methods,an adaptive Lasso grey model algorithm for regional FDI(foreign direct investment)prediction is proposed in this paper,and its validity is analyzed.Firstly,the characteristics of the FDI data in six provinces of Central China are generalized,and the mixture model’s constituent variables of the Lasso grey problem as well as the grey model are defined.next,based on the influencing factors of regional FDI statistics(mean values of regional FDI and median values of regional FDI),an adaptive Lasso grey model algorithm for regional FDI was established.Then,an application test in Central China is taken as a case study to illustrate the feasibility of the adaptive Lasso grey model algorithm in regional FDI prediction.We also select RMSE(root mean square error)and MAE(mean absolute error)to demonstrate the convergence and the validity of the algorithm.Finally,we train this proposedal gorithm according to the regional FDI statistical data in six provinces in Central China from 2006 to 2018.We then use it to predict the regional FDI statistical data from 2019 to 2023 and show its changing tendency.The extended work for the adaptive Lasso grey model algorithm and its procedure to other regional economic fields is also discussed.

关键词： Adaptive lasso grey model algorithm regional FDI statistics mean value of regional FDI median value of regional FDI

AF-net:A Medical Image Segmentation network Based on Attention Mechanism and Feature Fusion

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Computers, Materials & Continua 2021年第11期69卷 1877-1891页

作者： Guimin Hou Jiaohua Qin Xuyu Xiang Yun Tan Neal N.Xiong College of Computer Science and Information Technology Central South University of Forestry&TechnologyChangsha410004China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464OKUSA

Medical image segmentation is an important application field of computer vision in medical image processing.Due to the close location and high similarity of different organs in medical images,the current segmentation algorithms have problems with mis-segmentation and poor edge segmentation.To address these challenges,we propose a medical image segmentation network(AF-net)based on attention mechanism and feature fusion,which can effectively capture global information while focusing the network on the object area.In this approach,we add dual attention blocks(DA-block)to the backbone network,which comprises parallel channels and spatial attention branches,to adaptively calibrate and weigh features.Secondly,the multi-scale feature fusion block(MFF-block)is proposed to obtain feature maps of different receptive domains and get multi-scale information with less computational consumption.Finally,to restore the locations and shapes of organs,we adopt the global feature fusion blocks(GFF-block)to fuse high-level and low-level information,which can obtain accurate pixel positioning.We evaluate our method on multiple datasets(the aorta and lungs dataset),and the experimental results achieve 94.0%in mIoU and 96.3%in DICE,showing that our approach performs better than U-net and other state-of-art methods.

关键词： Deep learning medical image segmentation feature fusion attention mechanism

Image Recognition of Citrus Diseases Based on Deep Learning

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Computers, Materials & Continua 2021年第1期66卷 457-466页

作者： Zongshuai Liu Xuyu Xiang Jiaohua Qin Yun Tan Qin Zhang Neal N.Xiong Central South University of Forestry and Technology Changsha410004China School of Information Technology and Management Hunan University of Finance and EconomicsChangsha410205China Department of Mathematics and Computer Science Northeastern State UniversityTahlequahOK74464USA

In recent years,with the development of machine learning and deep learning,it is possible to identify and even control crop diseases by using electronic devices instead of manual observation.In this paper,an image recognition method of citrus diseases based on deep learning is proposed.We built a citrus image dataset including six common citrus diseases.The deep learning network is used to train and learn these images,which can effectively identify and classify crop diseases.In the experiment,we use MobilenetV2 model as the primary network and compare it with other network models in the aspect of speed,model size,accuracy.Results show that our method reduces the prediction time consumption and model size while keeping a good classification accuracy.Finally,we discuss the significance of using MobilenetV2 to identify and classify agricultural diseases in mobile terminal,and put forward relevant suggestions.

关键词： Deep learning image classification citrus diseases agriculture science and technology

Reversible Data Hiding in Encrypted Images Based on Adaptive Prediction and Labeling

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Computers, Materials & Continua 2022年第11期73卷 3613-3628页

作者： Jiaohua Qin Zhibin He Xuyu Xiang Neal N.Xiong College of Computer Science and Information Technology Central South University of Forestry&TechnologyChangsha410004China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464OKUSA

Recently,reversible data hiding in encrypted images(RDHEI)based on pixel prediction has been a hot topic.However,existing schemes still employ a pixel predictor that ignores pixel changes in the diagonal direction during prediction,and the pixel labeling scheme is inflexible.To solve these problems,this paper proposes reversible data hiding in encrypted images based on adaptive prediction and labeling.First,we design an adaptive gradient prediction(AGP),which uses eight adjacent pixels and combines four scanning methods(i.e.,horizontal,vertical,diagonal,and diagonal)for prediction.AGP can adaptively adjust the weight of the linear prediction model according to the weight of the edge attribute of the pixel,which improves the prediction ability of the predictor for complex images.At the same time,we adopt an adaptive huffman coding labeling scheme,which can adaptively generate huffman codes for labeling according to different images,effectively improving the scheme’s embedding performance on the dataset.The experimental results show that the algorithm has a higher embedding rate.The embedding rate on the test image Jetplane is 4.2102 bpp,and the average embedding rate on the image dataset Bossbase is 3.8625 bpp.

关键词： Reversible data hiding adaptive gradient prediction huffman coding embedding capacity

news Text Topic Clustering Optimized Method Based on TF-IDF Algorithm on Spark

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Computers, Materials & Continua 2020年第1期62卷 217-231页

作者： Zhuo Zhou Jiaohua Qin Xuyu Xiang Yun Tan Qiang Liu Neal N.Xiong College of Computer Science and Information Technology Central South University of Forestry&TechnologyChangsha410114China Department of Mathematics and Computer Science Northeastern State UniversityOK74464USA

Due to the slow processing speed of text topic clustering in stand-alone architecture under the background of big data,this paper takes news text as the research object and proposes LDA text topic clustering algorithm based on Spark big data platform.Since the TF-IDF(term frequency-inverse document frequency)algorithm under Spark is irreversible to word mapping,the mapped words indexes cannot be traced back to the original words.In this paper,an optimized method is proposed that TF-IDF under Spark to ensure the text words can be restored.Firstly,the text feature is extracted by the TF-IDF algorithm combined CountVectorizer proposed in this paper,and then the features are inputted to the LDA(Latent Dirichlet Allocation)topic model for training.Finally,the text topic clustering is obtained.Experimental results show that for large data samples,the processing speed of LDA topic model clustering has been improved based Spark.At the same time,compared with the LDA topic model based on word frequency input,the model proposed in this paper has a reduction of perplexity.

关键词： news text topic clustering spark platform countvectorizer algorithm TF-IDF algorithm latent dirichlet allocation model

Coverless Video Steganography Based on Frame Sequence Perceptual Distance Mapping

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Computers, Materials & Continua 2022年第10期73卷 1571-1583页

作者： Runze Li Jiaohua Qin Yun Tan Neal N.Xiong College of Computer Science and Information Technology Central South University of Forestry&TechnologyChangsha410004China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464OKUSA

Most existing coverless video steganography algorithms use a particular video frame for information hiding.These methods do not reflect the unique sequential features of video carriers that are different from image and have poor robustness.We propose a coverless video steganography method based on frame sequence perceptual distance mapping.In this method,we introduce Learned Perceptual Image Patch Similarity(LPIPS)to quantify the similarity between consecutive video frames to obtain the sequential features of the video.Then we establish the relationship map between features and the hash sequence for information hiding.In addition,the MongoDB database is used to store the mapping relationship and speed up the index matching speed in the information hiding process.Experimental results show that the proposed method exhibits outstanding robustness under various noise attacks.Compared with the existing methods,the robustness to Gaussian noise and speckle noise is improved by more than 40%,and the algorithm has better practicability and feasibility.

关键词： Coverless steganography frame sequence perception distance

Risk Prediction of Aortic Dissection Operation Based on Boosting Trees

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Computers, Materials & Continua 2021年第11期69卷 2583-2598页

作者： Ling Tan Yun Tan Jiaohua Qin Hao Tang Xuyu Xiang Dongshu Xie Neal N.Xiong The Second Xiangya Hospital of Central South University Changsha410011HunanChina Central South University of Forestry&Technology Changsha410004HunanChina northeastern State University Tahlequah74464OKUSA

During the COVID-19 pandemic,the treatment of aortic dissection has faced additional challenges.The necessary medical resources are in serious shortage,and the preoperative waiting time has been significantly prolonged due to the requirement to test for COVID-19 infection.In this work,we focus on the risk prediction of aortic dissection surgery under the influence of the COVID-19 pandemic.A general scheme of medical data processing is proposed,which includes five modules,namely problem definition,data preprocessing,data mining,result analysis,and knowledge application.Based on effective data preprocessing,feature analysis and boosting trees,our proposed fusion decision model can obtain 100%accuracy for early postoperative mortality prediction,which outperforms machine learning methods based on a single model such as LightGBM,XGBoost,and CatBoost.The results reveal the critical factors related to the postoperative mortality of aortic dissection,which can provide a theoretical basis for the formulation of clinical operation plans and help to effectively avoid risks in advance.

关键词： Risk prediction aortic dissection COVID-19 postoperative mortality boosting tree

Criss-Cross Attentional Siamese networks for Object Tracking

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Computers, Materials & Continua 2022年第11期73卷 2931-2946页

作者： Zhangdong Wang Jiaohua Qin Xuyu Xiang Yun Tan Neal N.Xiong College of Computer Science and Information Technology Central South University of Forestry&TechnologyChangsha410004China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464OKUSA

Visual object tracking is a hot topic in recent years.In the meanwhile,Siamese networks have attracted extensive attention in this field because of its balanced precision and speed.However,most of the Siamese network methods can only distinguish foreground from the non-semantic background.The fine-tuning and retraining of fully-convolutional Siamese networks for object tracking(SiamFC)can achieve higher precision under interferences,but the tracking accuracy is still not ideal,especially in the environment with more target interferences,dim light,and shadows.In this paper,we propose crisscross attentional Siamese networks for object tracking(SiamCC).To solve the imbalance between foreground and non-semantic background,we use the feature enhancement module of criss-cross attention to greatly improve the accuracy of video object tracking in dim light and shadow environments.Experimental results show that the maximum running speed of SiamCC in the object tracking benchmark dataset is 90 frames/second.In terms of detection accuracy,the accuracy of shadow sequences is greatly improved,especially the accuracy score of sequence HUMAn8 is improved from 0.09 to 0.89 compared with the original SiamFC,and the success rate score is improved from 0.07 to 0.55.

关键词： Criss-cross attention object-tracking siamese-network

Safety Analysis of Riding at Intersection Entrance Using Video Recognition Technology

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Computers, Materials & Continua 2022年第9期72卷 5135-5148页

作者： Xingjian Xue Linjuan Ge Longxin Zeng Weiran Li Rui Song Neal N.Xiong College of Landscape Architecture Central South University of Forestry and TechnologyChangshaHunan410004China College of Logistics and Transportation Central South University of Forestry and TechnologyChangshaHunan410004China Department of Mathematics and Computer Science Northeastern State UniversityTahlequahOK74464United States

To study riding safety at intersection entrance,video recognition technology is used to build vehicle-bicycle conflict models based on the Bayesian method.It is analyzed the relationship among the width of nonmotorized lanes at the entrance lane of the intersection,the vehicle-bicycle soft isolation form of the entrance lane of intersection,the traffic volume of right-turning motor vehicles and straight-going non-motor vehicles,the speed of right-turning motor vehicles,and straight-going non-motor vehicles,and the conflict between right-turning motor vehicles and straight-going nonmotor vehicles.Due to the traditional statistical methods,to overcome the discreteness of vehicle-bicycle conflict data and the differences of influencing factors,the Bayesian random effect Poisson-log-normal model and random effect negative binomial regression model are established.The results show that the random effect Poisson-log-normal model is better than the negative binomial distribution of random effects;The width of non-motorized lanes,the form of vehicle-bicycle soft isolation,the traffic volume of right-turning motor vehicles,and the coefficients of straight traffic volume obey a normal distribution.Among them,the type of vehicle-bicycle soft isolation facilities and the vehicle-bicycle traffic volumes are significantly positively correlated with the number of vehicle-bicycle conflicts.The width of non-motorized lanes is significantly negatively correlated with the number of vehicle-bicycle conflicts.Peak periods and flat periods,the average speed of right-turning motor vehicles,and the average speed of straight-going non-motor vehicles have no significant influence on the number of vehicle-bicycle conflicts.

关键词： Video recognition technology vehicle-bicycle conflict intersection entrance random effect poisson-log-normal model random effect negative binomial regression model