Journal of Intelligent Systems最新文献

{"title":"Periodic analysis of scenic spot passenger flow based on combination neural network prediction model","authors":"Fang Yin","doi":"10.1515/jisys-2023-0158","DOIUrl":"https://doi.org/10.1515/jisys-2023-0158","url":null,"abstract":"\u0000 To prevent in a short time the rapid increase of tourists and corresponding traffic restriction measures’ lack in scenic areas, this study established a prediction model based on an improved convolutional neural network (CNN) and long- and short-term memory (LSTM) combined neural network. The study used this to predict the inflow and outflow of tourists in scenic areas. The model uses a residual unit, batch normalization, and principal component analysis to improve the CNN. The experimental results show that the model works best when batches’ quantity is 10, neurons’ quantity in the LSTM layer is 50, and the number of iterations is 50 on a workday; on non-working days, it is best to choose 10, 100, or 50. Using root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) as evaluation indicators, the inflow and outflow RMSEs of this study model are 82.51 and 89.80, MAEs are 26.92 and 30.91, NRMSEs are 3.99 and 3.94, and MAPEs are 1.55 and 1.53. Among the various models, this research model possesses the best prediction function. This provides a more accurate prediction method for the prediction of visitors’ flow rate in scenic spots. Meanwhile, the research model is also conducive to making corresponding flow-limiting measures to protect the ecology of the scenic area.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140520728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online English writing teaching method that enhances teacher–student interaction","authors":"Yaqiu Jiang","doi":"10.1515/jisys-2023-0235","DOIUrl":"https://doi.org/10.1515/jisys-2023-0235","url":null,"abstract":"\u0000 A significant component of the online learning platform is the online exercise assessment system, which has access to a wealth of past student exercise data that may be used for data mining research. However, the data from the present online exercise system is not efficiently used, making each exercise less relevant for students and decreasing their interest and interaction with the teacher as she explains the activities. In light of this, this research creates an exercise knowledge map based on the connections between workouts, knowledge points, and previous tournaments. The neural matrix was then improved using cross-feature sharing and feature augmentation units to deconstruct the workout recommendation model. The study also developed an interactive text sentiment analysis model based on the expansion of the self-associative word association network to assess how students interacted after the introduction of the personalized exercise advice teaching approach. The outcomes demonstrated that the suggested model’s mean diversity value at completion was 0.93, an increase of 0.14 and 0.23 over collaborative filtering algorithm and DeepFM (deep factor decompose modle), respectively, and that the proposed model’s final convergence value was 92.3%, an improvement of 2.3 and 4.1% over the latter two models. The extended model used in the study outperformed the support vector machine (SVM) and Random Forest models in terms of accuracy by 5.9 and 1.7%, respectively. In terms of F1 value indicator, the model proposed by the research has a value of 90.4%, which is 2.5 and 2.1% higher than the SVM model and Random Forest model; in terms of recall rate indicators, the model proposed by the research institute has a value of 94.3%, which is an increase of 6.2 and 9.8% compared to the latter two models. This suggests that the study’s methodology has some application potential and is advantageous in terms of customized recommendation and interactive sentiment recognition.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140525311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural network big data fusion in remote sensing image processing technology","authors":"Xiaobo Wu","doi":"10.1515/jisys-2023-0147","DOIUrl":"https://doi.org/10.1515/jisys-2023-0147","url":null,"abstract":"\u0000 Remote sensing (RS) image processing has made significant progress in the past few years, but it still faces some problems such as the difficulty in processing large-scale RS image data, difficulty in recognizing complex background, and low accuracy and efficiency of processing. In order to improve the existing problems in RS image processing, this study dealt with ConvNext-convolutional neural network (CNN) and big data (BD) in parallel. Moreover, it combined the existing RS image processing with the high dimensional analysis of data and other technologies. In this process, the parallel processing of large data and high-dimensional data analysis technology improves the difficulty and low efficiency of large-scale RS image data processing in the preprocessing stage. The ConvNext-CNN optimizes the two modules of feature extraction and object detection in RS image processing, which improves the difficult problem of complex background recognition and improves the accuracy of RS image processing. At the same time, the performance of RS image processing technology after neural networks (NNs) and BD fusion and traditional RS image processing technology in many aspects are analyzed by experiments. In this study, traditional RS image processing and RS image processing combined with NN and BD were used to process 2,328 sample datasets. The image processing accuracy and recall rate of traditional RS image processing technology were 81 and 82%, respectively, and the F1 score was about 0.81 (F1 value is the reconciled average of accuracy and recall, a metric that combines accuracy and recall to evaluate the quality of the results, a higher F1 value indicates a better overall performance of the retrieval system). The accuracy rate and recall rate of RS image processing technology, which integrates NN and BD, were 97 and 98%, respectively, and its F1 score was about 0.97. After analyzing the process of these experiments and the final output results, it can be determined that the RS image processing technology combined with NN and BD can improve the problems of large-scale data processing difficulty, recognition difficulty under complex background, low processing accuracy and efficiency. In this study, the RS image processing technology combined with NN and BD has stronger adaptability with the help of NN and BD technology, and can adjust parameters and can be applied in more tasks.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140526869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the construction and reform path of online and offline mixed English teaching model in the internet era","authors":"Ying Lan","doi":"10.1515/jisys-2023-0230","DOIUrl":"https://doi.org/10.1515/jisys-2023-0230","url":null,"abstract":"\u0000 The Internet era resulted in the rise and advancement of MOOK, WeChat, and mobile networks, making it possible to expand English teaching methods. However, the English teaching industry has the problem of not valuing students’ personalized cognition, and the accuracy of teaching resource delivery is low. Therefore, the research uses the noise gate analysis method to design a cognitive diagnostic model for students and designs an English teaching resource recommendation model in view of a convolutional joint probability matrix (JPM) decomposition algorithm. The research results showed that the cognitive diagnostic model designed in the study had a higher accuracy. Compared to traditional algorithms, the overall recommendation effect of the English teaching resource recommendation model had an average improvement of 11.63% and compared to the JPM algorithm combined with cognitive diagnosis (CD), the overall recommendation effect value had an average improvement of 1.977%. When recommending complex teaching resources, the recommendation effect value had an average improvement of 11.54% compared to traditional algorithms, and the overall average improvement was 1.877% compared to the JPM algorithm combined with CD. In the experimental group, with the assistance of the research algorithm, students’ grades improved by an average of 2.38 points, which was significantly higher than the 0.89 points in the control group. The experiment showcases that the CD and recommendation model designed by the research has higher accuracy, can help improve the efficiency of teaching resource recommendation, reduces teaching costs, and has certain application value.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140520852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved rapidly exploring random tree using salp swarm algorithm","authors":"Dena Kadhim Muhsen, Firas Abdulrazzaq Raheem, Ahmed T. Sadiq","doi":"10.1515/jisys-2023-0219","DOIUrl":"https://doi.org/10.1515/jisys-2023-0219","url":null,"abstract":"\u0000 Due to the limitations of the initial rapidly exploring random tree (RRT) algorithm, robotics faces challenges in path planning. This study proposes the integration of the metaheuristic salp swarm algorithm (SSA) to enhance the RRT algorithm, resulting in a new algorithm termed IRRT-SSA. The IRRT-SSA addresses issues inherent in the original RRT, enhancing efficiency and path-finding capabilities. A detailed explanation of IRRT-SSA is provided, emphasizing its distinctions from the core RRT. Comprehensive insights into parameterization and algorithmic processes contribute to a thorough understanding of its implementation. Comparative analysis demonstrates the superior performance of IRRT-SSA over the basic RRT, showing improvements of approximately 49, 54, and 54% in average path length, number of nodes, and number of iterations, respectively. This signifies the enhanced effectiveness of the proposed method. Theoretical and practical implications of IRRT-SSA are highlighted, particularly its influence on practical robotic applications, serving as an exemplar of tangible benefits.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140527360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time semantic segmentation based on BiSeNetV2 for wild road","authors":"Honghuan Chen, Xiaoke Lan","doi":"10.1515/jisys-2023-0205","DOIUrl":"https://doi.org/10.1515/jisys-2023-0205","url":null,"abstract":"\u0000 State-of-the-art segmentation models have shown great performance in structured road segmentation. However, these models are not suitable for the wild roads, which are highly unstructured. To tackle the problem of real-time semantic segmentation of wild roads, we propose a Multi-Information Concatenate Network based on BiSeNetV2 and construct a segmentation dataset Dalle Molle institute for artificial intelligence feature segmentation (IDSIAFS) based on Dalle Molle institute for artificial intelligence. The proposed model removes structural redundancy and optimizes the semantic branch based on BiSeNetV2. Moreover, the Dual-Path Semantic Inference Layer (TPSIL) reduces computation by designing the channel dimension of the semantic branch feature map and aggregates feature maps of different depths. Finally, the segmentation results are achieved by fusing both shallow detail information and deep semantic information. Experiments on the IDSIAFS dataset demonstrate that our proposed model achieves an 89.5% Intersection over Union. The comparative experiments on Cityscapes and India driving dataset benchmarks show that proposed model achieves good inference accuracy and faster inference speed.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140527424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of machine learning ridge regression models using Monte Carlo, bootstrap, and variations in cross-validation","authors":"Robbie T. Nakatsu","doi":"10.1515/jisys-2022-0224","DOIUrl":"https://doi.org/10.1515/jisys-2022-0224","url":null,"abstract":"Abstract In recent years, there have been several calls by practitioners of machine learning to provide more guidelines on how to use its methods and techniques. For example, the current literature on resampling methods is confusing and sometimes contradictory; worse, there are sometimes no practical guidelines offered at all. To address this shortcoming, a simulation study was conducted that evaluated ridge regression models fitted on five real-world datasets. The study compared the performance of four resampling methods, namely, Monte Carlo resampling, bootstrap, k-fold cross-validation, and repeated k-fold cross-validation. The goal was to find the best-fitting λ (regularization) parameter that would minimize mean squared error, by using nine variations of these resampling methods. For each of the nine resampling variations, 1,000 runs were performed to see how often a good fit, average fit, and poor fit λ value would be chosen. The resampling method that chose good fit values the greatest number of times was deemed the best method. Based on the results of the investigation, three general recommendations are made: (1) repeated k-fold cross-validation is the best method to select as a general-purpose resampling method; (2) k = 10 folds is a good choice in k-fold cross-validation; (3) Monte Carlo and bootstrap are underperformers, so they are not recommended as general-purpose resampling methods. At the same time, no resampling method was found to be uniformly better than the others.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78871554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HWCD: A hybrid approach for image compression using wavelet, encryption using confusion, and decryption using diffusion scheme","authors":"H. R. Latha, Alagarswamy Ramaprasath","doi":"10.1515/jisys-2022-9056","DOIUrl":"https://doi.org/10.1515/jisys-2022-9056","url":null,"abstract":"Abstract Image data play important role in various real-time online and offline applications. Biomedical field has adopted the imaging system to detect, diagnose, and prevent several types of diseases and abnormalities. The biomedical imaging data contain huge information which requires huge storage space. Moreover, currently telemedicine and IoT based remote health monitoring systems are widely developed where data is transmitted from one place to another. Transmission of this type of huge data consumes more bandwidth. Along with this, during this transmission, the attackers can attack the communication channel and obtain the important and secret information. Hence, biomedical image compression and encryption are considered the solution to deal with these issues. Several techniques have been presented but achieving desired performance for combined module is a challenging task. Hence, in this work, a novel combined approach for image compression and encryption is developed. First, image compression scheme using wavelet transform is presented and later a cryptography scheme is presented using confusion and diffusion schemes. The outcome of the proposed approach is compared with various existing techniques. The experimental analysis shows that the proposed approach achieves better performance in terms of autocorrelation, histogram, information entropy, PSNR, MSE, and SSIM.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78967186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-sensor remote sensing image alignment based on fast algorithms","authors":"Tao Shu","doi":"10.1515/jisys-2022-0289","DOIUrl":"https://doi.org/10.1515/jisys-2022-0289","url":null,"abstract":"Abstract Remote sensing image technology to the ground has important guiding significance in disaster assessment and emergency rescue deployment. In order to realize the fast automatic registration of multi-sensor remote sensing images, the remote sensing image block registration idea is introduced, and the image reconstruction is processed by using the conjugate gradient descent (CGD) method. The scale-invariant feature transformation (SIFT) algorithm is improved and optimized by combining the function-fitting method. By this way, it can improve the registration accuracy and efficiency of multi-sensor remote sensing images. The results show that the average peak signal-to-noise ratio of the image processed by the CGD method is 25.428. The average root mean square value is 17.442. The average image processing time is 6.093 s. These indicators are better than the passive filter algorithm and the gradient descent method. The average accuracy of image registration of the improved SIFT registration method is 96.37%, and the average image registration time is 2.14 s. These indicators are significantly better than the traditional SIFT algorithm and speeded-up robust features algorithm. It is proved that the improved SIFT registration method can effectively improve the accuracy and operation efficiency of multi-sensor remote sensing image registration methods. The improved SIFT registration method effectively solves the problems of low accuracy and long time consumption of traditional multi-sensor remote sensing image fast registration methods. While maintaining high registration accuracy, it improves the image registration speed and provides technical support for a rapid disaster assessment after major disasters such as earthquakes and floods. And it has an important value for the development of the efficient post-disaster rescue deployment.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82616252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intelligent algorithm for fast machine translation of long English sentences","authors":"Hengheng He","doi":"10.1515/jisys-2022-0257","DOIUrl":"https://doi.org/10.1515/jisys-2022-0257","url":null,"abstract":"Abstract Translation of long sentences in English is a complex problem in machine translation. This work briefly introduced the basic framework of intelligent machine translation algorithm and improved the long short-term memory (LSTM)-based intelligent machine translation algorithm by introducing the long sentence segmentation module and reordering module. Simulation experiments were conducted using the public corpus and the local corpus containing self-collected linguistic data. The improved algorithm was compared with machine translation algorithms based on a recurrent neural network and LSTM. The results suggested that the LSTM-based machine translation algorithm added with the long sentence segmentation module and reordering module effectively segmented long sentences and translated long English sentences more accurately, and the translation was more grammatically correct.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83202682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}