bluuebunny/crossref_metadata_2025_split huggingface dataset:
Portable Kiwi Variety Classification Equipment Based on Transfer Learning
Authors: Wang Qilong , Zhao Lijiang , Niu Zijie 
Published: April 2021

Abstract
China is a one of the largest countries producing kiwifruit, and there are many kinds of kiwifruit in China. The identification of kiwifruit varieties has important application value in kiwifruit automatic picking robots and kiwifruit sorting factories. The paper studies the classification of kiwifruit varieties based on deep learning, and studies the deployment and application of kiwifruit classification model on the Jetson Nano artificial intelligence development board. A portable device used for classification of kiwifruit varieties was designed and developed. This paper studied the selection of various parts of the portable device of kiwifruit classification and designed the portable device shell. The portable device shell was obtained through the 3D printer, assembled the parts, and obtained the portable kiwi variety classification equipment. The construction of the kiwifruit dataset of “Cuixiang”, “Qinmei”, “Xuxiang” and “Hayward” kiwis was studied and using data augmentation to introduce variations into the data. According to the characteristics of the problem of 4 types of kiwifruit classification, the methods and advantages of transfer learning are discussed. Using transfer learning based on three pre-training models Xception, ResNet50, DenseNet121. Comparative analysis of the model size, training speed, convergence, and recognition accuracy of the transfer learning model based on Xception, ResNet50, and DenseNet121, and concluded that in Xception, ResNet50, and DenseNet121, the transfer learning model based on DenseNet121 pre-trained model has the best effect on the classification of four kiwifruit varieties, with fast convergence speed, smallest model, and high recognition accuracy of 97.79%. This paper studied the deployment and experiment of the deep learning model on the Jetson Nano development board. Using the PyTorch to perform transfer learning based on the DenseNet121 pre-training model. After using the TensorRT acceleration engine, it took an average of 0.035s to classify each image, and the real-time classification FPS reached an average of 30, the accuracy rate of the model on the validation set is 93.63%, achieving fast and accurate classification of kiwi varieties.
Simulation Prediction and Control Strategy of COVID-19 Dynamic Contact Network in USA and Various States -- Based on Effective Regeneration Number and Improved Discrete SEIQDHR Model
Authors: Li Yichi , Liu Chenglin , Zhao Suling , Wang Zhen , Zhao Huan , Chu Shenglan , Xiong Zhouqiang , Geng Liang 
Published: April 2021

AbstractPurpose: Based on the latest characteristics of the transmission mechanism of the COVID-19 epidemic in the United States, this article improves the classic dynamics model of the spread of infectious diseases, simulates and predicts the future trend of the COVID-19 epidemic in the United States and various states. According to the computer program of COVID-19 dynamic contact network, the results provide effective control strategies for the future epidemic prevention work of the United States. Method: The SEIR model is improved by the latest effective reproduction number of the COVID-19 epidemic in the United States, and an improved discrete SEIQDHR model is established for the spread of the COVID-19 epidemic in the United States and various states. MATLAB software is used to perform least square fitting of key parameters, and the computer simulation process of COVID-19 dynamic contact network is solved dynamically. Results: The improved discrete SEIQDHR model is reliable in the analysis of the spread of infectious diseases. The model well simulates the current dynamic contact network of the COVID-19 epidemic in the United States. The results of computer program show that mid-to-late November 2020 is the period with the largest number of new diagnoses before the end of the epidemic in the United States. In a long period of time in the future, the cumulative number of confirmed cases in the United States and states will continue to show a clear upward trend. The cumulative number of confirmed cases of the COVID-19 epidemic in the United States will reach its peak in early December 2021, which is about 37.11 million; the cumulative number of confirmed cases in Texas will reach its peak on March 15, 2021, about 3.21 million. Controlling the source of infection, blocking the route of transmission and strengthening the tracking and isolation are still effective measures to prevent and control the epidemic.
Analysis of key technology of computer artificial intelligence recognition
Authors: Wang Zekai , Shi Zekai , Zhang Mingqi 
Published: April 2021

Abstract
With the continuous development of science and technology, people for the research and development of artificial intelligence gradually in-depth, intelligent technology is widely used, can help people to improve the state of life, improve people’s quality of life. This paper will analyze the computer artificial intelligence identification technology, explore its practical application in life, and put forward some suggestions for the development of intelligent technology.
Dense Feature Fusion for Online Mutual Knowledge Distillation
Authors: Ni Dongsheng 
Published: April 2021

Abstract
Feature maps contain rich information about image intensity and spatial correlation. However, previous online knowledge distillation methods only utilize the class probabilities, Ignoring the middle-level supervision, resulting in low efficiency in training many models. Even if some methods have joined the middle-level supervision, the previous way is to define the characteristic loss, and the effect is general. We propose a new method of middle-level supervision, through the fusion of features between teacher academic network to enter the supervision. The specific method is to fuse the features of the teacher academic network, and establish an auxiliary fusion branch to process the fusion information, so that the feature fusion can effectively strengthen the feature interaction of the teacher academic network. At the same time, we added the normalized integrated distillation of the output, and our method reached the SOTA in online KD. We have done a lot of experiments on cifar-10, cifar-100 and ImageNet datasets, and proved that this method is more effective than other methods in the performance of sub network and fusion classifier, as well as generating meaningful feature maps.
The Model Design of Medical Data Life Cycle Based on Big Data Platform
Authors: Li Ziqing , Pei Shenglei , Feng Guiliang 
Published: April 2021

Abstract
With the gradual popularization of the mobile Internet and the Internet of Things, people’s lifestyles have undergone profound changes. In the medical field, the demand for smart medical remote care is gradually increasing. The continuous upgrading and updating of medical equipment and the precision operation of inspection equipment have made medical information systems contain more and more types of data. Based on data lifecycle management theory and big data processing technology such as Hadoop, this paper designs a corresponding data lifecycle management model for children’s chronic kidney disease remote rehabilitation system. Its role is to manage the data reasonably, thereby increasing the availability of information systems and improving the efficiency of system storage devices.
Research on the Integration Method of Intelligent Terminal Services Oriented to the Aged Cross-Border Platform
Authors: Feng Weijia , Yu Wenqing , Zhang Zheyu , Wang Juan , Tian Hongmei 
Published: April 2021

Abstract
To meet the needs of the elderly for smart, healthy, and elderly care, the paper uses the Internet, the Internet of Things, cloud computing, and other technical means to build a cloud platform-based smart health and elderly care cross-border platform service system. Designed and developed a health all-in-one smart terminal suitable for a home environment to realize physiological parameter collection and vital sign monitoring. This system promotes the intelligent upgrade of the cross-border platform for the elderly, and improves the quality and efficiency of the health of elderly services.
Research on Mathematical Genetic Algorithms Based on Evolutionary Computer Neural Network of Distribution Function Parameters
Authors: Han Yunna 
Published: April 2021

Abstract
Genetic algorithm and neural network are both ideas and algorithm skills. The flexibility of this method provides a stage for many researchers and applications. This paper discusses the neural network model based on the improved genetic algorithm under the parameters of the distribution function and its application in multi-variable, multi-step nonlinear economic forecasting. First of all, this article discusses the method of using improved genetic algorithm to learn neural network connection weights, and proposes a secondary optimization strategy. The main innovations or improvement techniques include: initial population generation strategy, crossover operation secondary optimization strategy, mutation operation Secondary optimization strategy, optimal chromosome inventory strategy, etc. This article uses VB for programming. The developed software has a certain versatility and can be used for prediction problems similar to nonlinear economic data.
Study on Intrusion detection model based on improved convolutional neural network
Authors: He Kaiyan 
Published: April 2021

Abstract
With the rapid development of information technology and cyberspace, information interactions between networks are becoming more frequent. At the same time, cyberattacks are posing more and more threats to network security through intrusions into computer systems or information systems. To address these problems, this paper proposes an improved convolutional neural network-based intrusion detection model (ICNN-IDS) to determine and classify specific types of intrusions after feature extraction and analysis of different network flow. The paper introduces the basic components of neural networks, including the convolutional layer, pooling layer and fully connected layer. Next, the experimental data set acquisition and pre-processing process are introduced, followed by the structural setup, the specific tuning process of the model, and the optimization of the model parameters are evaluated through experiments. In order to optimize the input feature matrix, the model adds a neuron mapping layer before the convolutional layer to convert sample data from 1D to 2D for the improvement of the model. The experimental results show that ICNN-IDS achieves 99.35% detection accuracy and a low false alarm rate of 0.21% on the KDD99 dataset with optimal parameter settings, which has significant improvement over existing detection models.
Long time prediction of human lower limb movement based on IPSO-BPNN
Authors: Jin Dongnan , Liu Yali , Ma Xunju , Song Qiuzhi 
Published: April 2021

Abstract
System delay caused by mechanical transmission, control calculation and data communication are the main factor affecting the man-machine collaborative control of lower extremity exoskeleton. Improved Particle Swarm Optimization Algorithm (IPSO) was proposed to optimize BPNN (Back Propagation Neural Network) to predict the future joint angle of human lower limb. The 3d motion capture system was used to collect the Angle data of human lower limb joints, and time span was added to reconstruct the time series, which was taken as the input of the model. Compared to PSO (Particle Swarm Optimization), IPSO added a three-route competitive optimization trajectory, the training feedback of BPNN and mutation operation, which accelerated the convergence of the algorithm and avoided local optimization. Besides, we established a prediction evaluation criterion with prediction duration, iteration efficiency, Root Mean Square Error (RMSE) and Determination Coefficient (DC) as the core to analyze the prediction results of BPNN, PSO-BPNN (Support Back Propagation Neural Network by Particle Swarm Optimization) and IPSO-BPNN (Support Back Propagation Neural Network by Improved Particle Swarm Optimization). The results show that the average RMSE of IPSO-BPNN is less than 0.75 and DC is more than 98%. IPSO-BPNN can make more accurate prediction of human lower limb joint angle, which is beneficial to improve the man-machine coordination performance of exoskeleton.
A Novel Structure-based Feature Extraction Approach for Financial Fraud Detection
Authors: Xia Hui , Ma Hui 
Published: April 2021

Abstract
In finance and economic area, financial fraud detection plays an important role for both corporate management and capital market system. Feature extraction is one of the most important procedure in fraudulent firm detection. Current feature extraction approaches pay large amounts of attention on their financial attributes, which have explicitly limited the representation of ‘normal’ pictures of firms, and furthermore, reduced the financial fraud detection performance. Hence it is necessary to search for a better set of functions as features to represent firms more accurately. In this work, notice that the imitation behaviors among firms often happen in business management, while this structure patterns have not been utilized in f inancial fraud detection so far, we extract features under the constraint of both financial characters and structure patterns of firms. We also design three measurements to quantify the structure patterns. Experimental results have shown a great performance of the proposed approach.
Application of Finite Difference Method in Image Reconstruction of Electrical Capacitance Tomography
Authors: Chen Huamin , Xue Xiao , Zheng Yangbing 
Published: April 2021

Abstract
Electrical capacitance tomography is to invert the medium distribution inside the object by using a limited set of measured capacitance values. In order to realize image reconstruction, the sensor sensitive field is solved by finite difference method under the condition of known sensor distribution and internal permittivity distribution of pipeline, and satisfactory results are obtained. According to the calculated sensitive field, the distribution image of the fluid on the fault plane is reconstructed by using the back-projection algorithm. Back-projection algorithm has the character of simple structure and fast speed of reconstructing images. However, the number of projections obtained by the capacitive tomography system is much smaller than the number of image pixels and the soft field characteristics of the imaging system make it difficult to accurately image, so only an approximate phase distribution image can be reconstructed.
Research on the application of Fama-French 5-factor model in the steel industry during COVID-19
Authors: Hou Dingwen , Chen Zirui 
Published: April 2021

Abstract
Based on the Fama-French 5-factor model (FF5), this paper analyzes the market changes of the American steel industry before and after the epidemic. In this paper, the Data from Kennethr French-Data Library were used to make multiple regression analysis of the U.S. steel industry before and after the epidemic. The Fama-French 5-factor model was also used for analysis. According to the results of multiple linear regression, due to the influence of COVID-19, the coefficient of market risk (Betam) decreased; Robust minus Week (RMW) changed from significant to insignificant; Small minus Big (SMB) and High minus Low (HML) were significant and the change of epidemic situation was not significant, while Conservative minus Aggressive (CMA) was not significant. The impact of the outbreak on the U.S. steel industry has been dramatic and has led to a huge decline in the entire industry.
The best input feature when using convolutional neural network for cough recognition
Authors: Cai Yunan , Xu Wenlong 
Published: April 2021

Abstract
In recent years, the use of convolutional neural networks has been successful in the task of cough recognition. This method mainly converts audio clips into the form of spectrograms, and then uses convolutional neural networks for classification, which prompts us to seek better input representation For more effective training to achieve better performance, in this article, we use STFT, Mel spectrogram, Log-Mel spectrogram and MFCC four different features as the input of the convolutional neural network, in the case of the same parameters Compare their performance below, where the Mel spectrum is used as input to achieve 92.5% classification accuracy. Secondly, we compared the classification performance of different Mel spectra generated by different window sizes and frame shifts under the same convolutional neural network. For a 320ms cough segment, the window size is 64, and the frame shift is 32, which has the best performance
Application of Collaborative Filtering Recommendation Algorithm in pharmacy system
Authors: Lv Yihang , Kong Jingjing 
Published: April 2021

Abstract
Recommendation algorithm has been widely used in Internet commerce. At present, the most widely used and successful algorithm is collaborative recommendation algorithm. It can be used in pharmacy marketing system based on user and project purpose. Based on the analysis of the current situation of retail drugstores, this paper constructs the marketing management system of retail drugstores, and establishes two recommendation models of the marketing system of retail drugstores and retail drugstores. The main goal of model 1 is to construct a user based collaborative filtering recommendation algorithm input, after the algorithm processing, to generate recommendation results, to achieve product recommendation to customers. The goal of model 2 is to construct a Project-based Collaborative filtering recommendation algorithm input, after the algorithm processing, produce the recommendation results, so as to find the target customers for the product. These two recommendation models are helpful to the research and improvement of pharmacy system algorithm.
Research on Efficient and Economical Power System Optimization Algorithm
Authors: Fei Qiu 
Published: April 2021

Abstract
The paper proposes to apply the particle swarm optimization algorithm to the dynamic optimization power flow calculation in the power system with distributed power generation, which takes all periods of time into consideration. The specific implementation method of the algorithm is given. According to the principle of nearby reactive power compensation, distributed power sources do not absorb reactive power from the system as much as possible. The reactive power required by distributed power sources is mainly provided by distributed power reactive power compensation devices. In order to verify the effectiveness of the algorithm, the economic load distribution of the power system with different unit numbers was tested, and compared with other optimization algorithms. The result proves that the algorithm can find the optimal solution efficiently and accurately, effectively avoiding the problem of falling into the local optimal, and ensuring a faster running speed.
Research on Vehicle Recognition Algorithm based on Convolution Neural Network
Authors: Zhang Pai , Chen Hanqing , Li Qinrui 
Published: April 2021

Abstract
In order to effectively identify vehicle types in intelligent transportation system, based on the analysis of Inception V3 model, a deep learning model of vehicle classification based on transfer learning theory is proposed in this paper. In this model, the last full connection layer is removed on the basis of Inception V3 model, and the parameter optimization layer is added, and then Dropout and global average pooling layer are adopted. Theoretical analysis and experimental results show that the performance of this model is better than that of VGG-16-based vehicle classification model, Xception-based vehicle classification model and Resnet-50-based vehicle classification model. The experimental results show that the training accuracy of the method proposed in this paper is more than 96.48% and the test accuracy is more than 83.86%.
Intelligent Path Planning Algorithm for UAV Group Based on Machine Learning
Authors: Li Ronghao , Xiong Yuhan , Zhang Tao 
Published: April 2021

Abstract
Aiming at the minimum and local minimum problems existing in the artificial potential field algorithm for obstacle avoidance of UAV, a new path planning method is proposed by using the improved artificial potential field algorithm. Different from the current artificial potential field method, this model starts from the interaction between two aircraft. On the basis of obstacle repulsion, the repulsion force between UAV is increased, and the pre-centroid of the cluster is defined as another source of gravity. The analysis of the algorithm shows that this method can effectively prevent the UAV from falling into the local minimum and enhance the control and obstacle avoidance ability of the UAV cluster. Based on the UAV control model, the path planning design is given and the simulation experiment is carried out. The experimental results show that the UAV cluster control based on this model has better obstacle avoidance performance and target tracking ability.
Identifying Clinical Detection in Disease Context by Computational Mining of PubMed
Authors: Fu Xiaoxue , Zhou Yi 
Published: April 2021

AbstractTo propose a novel informatics-based strategy for identifying candidate clinical tests for pancreatic cancer. We implement experiments on 6483, 27372, 28449 and 13017 publications respectively associated with risk, diagnosis, treatment and prognosis of this disease searched from PubMed and two query sets composed of 3880 clinical detection terms and 109 genes. By integrating gene-disease database, clinical information of tests, we determine the most likely terms related to the risk, diagnosis, treatment and prognosis of the disease and rank them according to our statistical scheme. We found 21, 42, 38 and 28 detection terms respectively related to risk, diagnosis, treatment and prognosis of pancreatic cancer. For risk assessment, “Adiponectin”, “Cholecystokinin (CCK)” and “Interleukin 2” with high New Term Frequency Inverse Document Frequency (NTFIDF) imply they take on greater importance to the documents where they occur in the corpus. “Carotene, Beta”,” Albumin, Serum” and “Prothrombin Fragment 1+2” with high Inverse Document Frequency (IDF) weightings indicate they provide more information. Furthermore, by incorporating 109 genes into query sets, we discovered “IGF Binding Protein-1 (IGFBP-1)” with the highest NTFIDF (0.088407) whereas “IGF Binding Protein-1 (IGFBP-1)”, “Leptin”, “Interleukin 2”, “5-Methyltetrahydrofolate” and “Gram” possessed high IDF (2.72). Similarly, for diagnosis, treatment and prognosis, “Cholecystokinin (CCK)”, “Gram”, “Neurotensin” gain the highest IDF while “Gastrin, Serum”, “Neurotensin”, “Gastrin, Serum” have the highest NTFIDF correspondingly. The findings suggest a unique opportunity for laboratory and clinical research and should be further validated in prospective research.
Research on System Dynamics Simulation of Building Materials Supply Chain
Authors: Chen Jianbiao , Zhong Jiezhi , Xie Baosheng 
Published: April 2021

Abstract
The bullwhip effect of supply chain under non-information sharing and information sharing environment is analyzed theoretically. The model of building materials supply chain in two environments is constructed, and the system dynamics flow chart is established by Vensim PLE simulation software. The correctness of the theoretical analysis is verified by an example simulation. The sensitivity analysis of transportation delay time and manufacturer’s production cycle shows that extending transportation time can reduce manufacturer’s inventory, but raw material inventory in transit will increase; Shortening the production cycle of producers under the information sharing environment is more conducive to reducing the bullwhip effect of the supply chain.