International Journal of Intelligent Systems最新文献

{"title":"Neuron Segmentation via a Frequency and Spatial Domain–Integrated Encoder–Decoder Network","authors":"Haixing Song,&nbsp;Xuqing Zeng,&nbsp;Guanglian Li,&nbsp;Rongqing Wu,&nbsp;Simin Liu,&nbsp;Fuyun He","doi":"10.1155/int/7026120","DOIUrl":"https://doi.org/10.1155/int/7026120","url":null,"abstract":"<div>\u0000 <p>Three-dimensional (3D) segmentation of neurons is a crucial step in the digital reconstruction of neurons and serves as an important foundation for brain science research. In neuron segmentation, the U-Net and its variants have showed promising results. However, due to their primary focus on learning spatial domain features, these methods overlook the abundant global information in the frequency domain. Furthermore, issues such as insufficient processing of contextual features by skip connections and redundant features resulting from simple channel concatenation in the decoder lead to limitations in accurately segmenting neuronal fiber structures. To address these problems, we propose an encoder–decoder segmentation network integrating frequency domain and spatial domain to enhance neuron reconstruction. To simplify the segmentation task, we first divide the neuron images into neuronal cubes. Then, we design 3D FregSNet, which leverages both frequency and spatial domain features to segment the target neurons within these cubes. Then, we introduce a multiscale attention fusion module (MAFM) that utilizes spatial and channel position information to enhance contextual feature representation. In addition, a feature selection module (FSM) is incorporated to adaptively select discriminative features from both the encoder and decoder, increasing the weight on critical neuron locations and significantly improving segmentation performance. Finally, the segmented nerve fiber cubes were assembled into complete neurons and digitally reconstructed using available neuron tracking algorithms. In experiments, we evaluated 3D FregSNet on two challenging 3D neuron image datasets (the BigNeuron dataset and the CWMBS dataset). Compared to other advanced segmentation methods, 3D FregSNet demonstrates more accurate extraction of target neurons in noisy and weakly visible neuronal fiber images, effectively improving the performance of 3D neuron segmentation and reconstruction.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/7026120","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143424170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"JPEG Image Steganography With Automatic Embedding Cost Learning","authors":"Jianhua Yang,&nbsp;Yi Liao,&nbsp;Fei Shang,&nbsp;Xiangui Kang,&nbsp;Yifang Chen,&nbsp;Yun-Qing Shi","doi":"10.1155/int/5309734","DOIUrl":"https://doi.org/10.1155/int/5309734","url":null,"abstract":"<div>\u0000 <p>A great challenge to steganography has arisen with the wide application of steganalysis methods based on convolutional neural networks (CNNs). To this end, embedding cost learning frameworks based on generative adversarial networks (GANs) has been proposed and achieved success for spatial image steganography. However, the application of GAN to JPEG steganography is still in the prototype stage; its antidetectability and training efficiency should be improved. In conventional steganography, research has shown that the side information calculated from the precover can be used to enhance security. However, it is hard to calculate the side information without the spatial domain image. In this work, an embedding cost learning framework for JPEG image steganography via a GAN (JS–GAN) has been proposed, the learned embedding cost can be further adjusted asymmetrically according to the estimated side information (ESI). Experimental results have demonstrated that the proposed method can automatically learn a content-adaptive embedding cost function, and using the ESI properly can effectively improve the security performance. For example, under the attack of a classic steganalyzer GFR with a quality factor of 75 and 0.4 bpnzAC, the proposed JS–GAN can increase the detection error by 2.58% over J-UNIWARD, and the ESI–aided version JS–GAN (ESI) can further increase the security performance by 11.25% over JS–GAN.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/5309734","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cryptocurrency Trend Prediction Through Hybrid Deep Transfer Learning","authors":"Kia Jahanbin,&nbsp;Mohammad Ali Zare Chahooki","doi":"10.1155/int/4211799","DOIUrl":"https://doi.org/10.1155/int/4211799","url":null,"abstract":"<div>\u0000 <p>The impact of sentiment analysis of comments on social networks such as X (Twitter) on the cryptocurrency market’s behavior has been proven. Also, traditional sentiment analysis and not considering the possible aspects of tweets can cause the deep model to be misleading in predicting the price trend of cryptocurrencies. In this research, a model using transfer learning and the combination of pretrained DistilBERT networks, BiGRU deep neural network, and attention layer is presented to analyze the sentiments based on the aspect of tweets and predict the price trend of eight cryptocurrencies. These tweets are the opinions of 70 cryptocurrency expert influencers. After preprocessing, these tweets are injected into the hybrid model of DistilBERT, BiGRU, and attention layer (HDBA) to extract the aspect and determine the polarity of each aspect. The output of the HDBA model is entered into the combined model of BiGRU and the attention layer (HBA) to predict the price trend of each cryptocurrency in intervals of 1–10 days. The output of the HBA model is the best time interval of the influence of the sentiments of tweets on the price trend of cryptocurrencies. The results show that the HDBA model has improved the performance of the aspect-based sentiment analysis task by an average of 3% in the benchmark datasets. The results of the HBA model also show that this model has been able to predict the best time frame of the impact of sentiments on the behavior of the cryptocurrency market with an average accuracy of 68% and a precision of 73%.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/4211799","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Min–Max Filtering and Exponential Fossa Optimization Algorithm–Based Parallel Convolutional Neural Network for Heart Disease Detection","authors":"Aathilakshmi S.,&nbsp;Balasubramaniam S.,&nbsp;Sivakumar T. A.,&nbsp;Lakshmi Chetana V.","doi":"10.1155/int/1409684","DOIUrl":"https://doi.org/10.1155/int/1409684","url":null,"abstract":"<div>\u0000 <p>Heart disease is a leading cause of death worldwide, affecting millions of lives each year. Earlier and more accurate heart disease detection helps people to save their valuable lives. Many existing systems remain costly and inaccurate. To overcome these issues, an exponential fossa optimization algorithm–based parallel convolutional neural network (EFOA-PCNN) is proposed in this paper for efficient heart disease detection. Initially, the heart disease data are allowed for data normalization, which is performed by min–max normalization. These normalized data are forwarded to the feature selection phase, which is conducted based on chord distance. Finally, heart disease detection is performed using a parallel convolutional neural network (PCNN) that is trained using the EFOA. Here, the EFOA is developed by the combination of the fossa optimization algorithm (FOA) and exponentially weighted moving average (EWMA). The performance of the proposed EFOA-PCNN is analysed by three metrics, such as specificity, sensitivity, and accuracy, and the <i>F</i>1 score that gained superior values of 91.95%, 91.76%, 91.86%, and 92.39%. These results highlight the robustness and reliability of the proposed method in comparison to traditional approaches.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1409684","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV Group Distribution Route Optimization Under Time-Varying Weather Network","authors":"Wanchen Jie,&nbsp;Cheng Pei,&nbsp;Hong Yan,&nbsp;Weitong Lin","doi":"10.1155/int/8682162","DOIUrl":"https://doi.org/10.1155/int/8682162","url":null,"abstract":"<div>\u0000 <p>The rapid advancement in unmanned aerial vehicle (UAV) technology has marked a transformative shift in various industries, with logistics distribution service being one of the prime sectors reaping the benefits. UAVs offer substantial benefits in speed, cost, and reach, promising to revolutionize logistics, especially in remote areas. On the one hand, they are poised to meet demands for quick and versatile delivery options. On the other hand, their deployment comes with challenges. Weather variabilities such as rainfall, wind speed, and the need for safe take-off intervals can compromise UAV safety and operation. Conventional route optimization often overlooks these dynamic factors, resulting in inefficient or unworkable delivery routes. The repeated time-consuming calculations are caused by repeated trials when making UAV group distribution plans. Recognizing these gaps, this study proposes a data representation to effectively transform the flight flyable area of UAVs into a time-varying network that maintains spatiotemporal connectivity and establishes a mathematical model that represents the complexities of UAV group distribution. Then, a multistage dynamic optimization algorithm specifically tailored for large-scale time-varying network distribution route search is designed to obtain the stable and optimal solution. Subsequent experimental validations on actual case datasets have confirmed the correctness, effectiveness, and adaptability of the algorithm. Benchmarking against traditional CPLEX methods demonstrated that the algorithm not only rivals the best solutions but does so with a 38.8 times increase in computational speed. When pitted against the shortest path Dijkstra and <i>A</i>^∗ algorithms, the method consistently outperformed, delivering solutions up to 3.5 times faster in large-scale applications. Moreover, the parameter sensitivity analysis is performed on the algorithm by adjusting the safe flight thresholds of rainfall and wind speed parameters and revealed that the performance of the algorithm has a strong positive correlation with the size of the time-varying network.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8682162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-View Deep Learning Model for Accurate Breast Cancer Detection in Mammograms","authors":"Dilawar Shah,&nbsp;Mohammad Asmat Ullah Khan,&nbsp;Mohammad Abrar,&nbsp;Muhammad Tahir","doi":"10.1155/int/7638868","DOIUrl":"https://doi.org/10.1155/int/7638868","url":null,"abstract":"<div>\u0000 <p>Breast cancer (BC) remains a major global health problem designed for early diagnosis and requires innovative solutions. Mammography is the most common method of detecting breast abnormalities, but it is difficult to interpret the mammogram due to the complexities of the breast tissue and tumor characteristics. The EfficientViewNet model is designed to overcome false predictions of BC. The model consists of two pathways designed to analyze breast mass characteristics from craniocaudal (CC) and mediolateral oblique (MLO) views. These pathways comprehensively analyze the characteristics of breast tumors from each view. The proposed study possesses several significant strengths, with a high <i>F</i>1 score and recall of 0.99. It shows the robust discriminatory ability of the proposed model compared to other state-of-the-art models. The study also explored the effects of different learning rates on the model’s training dynamics. It showed that the widely used stepwise reduction strategy of the learning rate played a key role in the convergence and performance of the model. It enabled fast early progress and careful fine-tuning of the learning rate as the model nears optimum. The model opens the door to achieving a high level of patient outcomes through a very rigorous methodology.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/7638868","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Internet of Medical Things for Depression: Current Advancements, Challenges, and Trends","authors":"Md Belal Bin Heyat,&nbsp;Deepak Adhikari,&nbsp;Faijan Akhtar,&nbsp;Saba Parveen,&nbsp;Hafiz Muhammad Zeeshan,&nbsp;Hadaate Ullah,&nbsp;Yun-Hsuan Chen,&nbsp;Lu Wang,&nbsp;Mohamad Sawan","doi":"10.1155/int/6801530","DOIUrl":"https://doi.org/10.1155/int/6801530","url":null,"abstract":"<div>\u0000 <p>We investigated the fusion of the Intelligent Internet of Medical Things (IIoMT) with depression management, aiming to autonomously identify, monitor, and offer accurate advice without direct professional intervention. Addressing pivotal questions regarding IIoMT’s role in depression identification, its correlation with stress and anxiety, the impact of machine learning (ML) and deep learning (DL) on depressive disorders, and the challenges and potential prospects of integrating depression management with IIoMT, this research offers significant contributions. It integrates artificial intelligence (AI) and Internet of Things (IoT) paradigms to expand depression studies, highlighting data science modeling’s practical application for intelligent service delivery in real-world settings, emphasizing the benefits of data science within IoT. Furthermore, it outlines an IIoMT architecture for gathering, analyzing, and preempting depressive disorders, employing advanced analytics to enhance application intelligence. The study also identifies current challenges, future research trajectories, and potential solutions within this domain, contributing to the scientific understanding and application of IIoMT in depression management. It evaluates 168 closely related articles from various databases, including Web of Science (WoS) and Google Scholar, after the rejection of repeated articles and books. The research shows that there is 48% growth in research articles, mainly focusing on symptoms, detection, and classification. Similarly, most research is being conducted in the United States of America, and the trend is increasing in other countries around the globe. These results suggest the essence of automated detection, monitoring, and suggestions for handling depression.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6801530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive Network Analysis of Lung Cancer Biomarkers Identifying Key Genes Through RNA-Seq Data and PPI Networks","authors":"Meshrif Alruily,&nbsp;Murtada K. Elbashir,&nbsp;Mohamed Ezz,&nbsp;Bader Aldughayfiq,&nbsp;Majed Abdullah Alrowaily,&nbsp;Hisham Allahem,&nbsp;Mohanad Mohammed,&nbsp;Elsayed Mostafa,&nbsp;Ayman Mohamed Mostafa","doi":"10.1155/int/9994758","DOIUrl":"https://doi.org/10.1155/int/9994758","url":null,"abstract":"<div>\u0000 <p>This study addresses the pressing need for improved lung cancer diagnosis and treatment by leveraging computational methods and omics data analysis. Lung cancer remains a leading cause of cancer-related deaths globally, highlighting the urgency for more effective diagnostic and therapeutic approaches. Current diagnostic methods, such as imaging and biopsies, suffer from limitations in sensitivity, specificity, and accessibility, often due to factors such as poor data quality, small sample sizes, and variability in data sources. These limitations highlight the necessity for the development of advanced noninvasive techniques. Computational methods utilizing omics data have shown promise in overcoming these challenges by comprehensively understanding the molecular pathways involved in lung cancer. We propose a novel approach that utilizes RNA-Seq data and employs LASSO regression with attention mechanisms to identify lung cancer biomarkers. Our results demonstrate the effectiveness of this approach in identifying potential biomarkers for lung cancer, including well-known genes such as TP53, EGFR, KRAS, ALK, and PIK3CA, validating the model’s ability to uncover key genes associated with lung cancer development and progression. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed significant associations of the identified genes with critical biological processes and pathways, including protein synthesis, folding, cell adhesion, gene regulation, and immune responses. The PPI network analysis, constructed using the STRING database and Cytoscape application, highlighted a highly interconnected interaction landscape, with central hub genes playing pivotal roles in lung cancer progression. RPSA emerged as a crucial hub gene, consistently identified across different centrality measures. This study sheds light on the potential of computational methods and omics data analysis in improving lung cancer diagnosis and treatment, offering new insights for future research directions and personalized medicine strategies.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/9994758","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Servo Performance of a Two-Degree-of-Freedom Rotary Table Using Intelligent Control Optimized by SSA–GA","authors":"Xinan Gao,&nbsp;Xiaorong Guan,&nbsp;Huibin Li,&nbsp;Zheng Wang,&nbsp;Jinyu Kang,&nbsp;Yanlong Yang","doi":"10.1155/int/7679830","DOIUrl":"https://doi.org/10.1155/int/7679830","url":null,"abstract":"<div>\u0000 <p>The two-degree-of-freedom rotation stage serves as a crucial component in ground unmanned platforms, and its servo performance is pivotal to the platform’s overall functionality. To enhance the servo performance of the two-degree-of-freedom rotation stage, we proposed a novel adaptive control approach: SSA–GA optimization of RBFNN integration into PID control. This method leverages the SSA–GA algorithm to optimize the parameters within the RBFNN, which is then seamlessly integrated into the PID control framework. This integration enables precise control of the two-degree-of-freedom rotation stage, overcoming the limitations of traditional PID controllers. By simulating various real-world situations such as step, noise, sinusoidal, transient excitation, impulse and modelling errors, it is demonstrated that the proposed control method achieves significant improvement in terms of control accuracy, fast response and robustness. It offers a more effective and reliable method for controlling the two-degree-of-freedom rotation stage, addressing the challenges posed by various interfering factors. Meanwhile, through comprehensive comparisons with various optimization algorithms, we have proven that SSA–GA has the shortest optimization time. Consequently, the proposed method exhibits excellent application potential and broad prospects for future applications.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/7679830","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long Short-Term Memory Wavelet Neural Network for Renewable Energy Generation Forecasting","authors":"Eliana Vivas,&nbsp;Héctor Allende-Cid,&nbsp;Lelys Bravo de Guenni,&nbsp;Aurelio F. Bariviera,&nbsp;Rodrigo Salas","doi":"10.1155/int/8890906","DOIUrl":"https://doi.org/10.1155/int/8890906","url":null,"abstract":"<div>\u0000 <p>Renewable energy forecasting is crucial for pollution prevention, management, and long-term sustainability. In response to the challenges associated with energy forecasting, the simultaneous deployment of several data-processing approaches has been used in a variety of studies in order to improve the energy–time-series analysis, finding that, when combined with the wavelet analysis, deep learning techniques can achieve high accuracy in energy forecasting applications. Consequently, we investigate the implementation of various wavelets within the structure of a long short-term memory neural network (LSTM), resulting in the new LSTM wavelet (LSTMW) neural network. In addition, and as an improvement phase, we modeled the uncertainty and incorporated it into the forecast so that systemic biases and deviations could be accounted for (LSTMW with luster: LSTMWL). The models were evaluated using data from six renewable power generation plants in Chile. When compared to other approaches, experimental results show that our method provides a prediction error within an acceptable range, achieving a coefficient of determination (<i>R</i>²) between 0.73 and 0.98 across different test scenarios, and a consistent alignment between forecasted and observed values, particularly during the first 3 prediction steps.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8890906","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}