Egyptian Informatics Journal最新文献

{"title":"A federated learning model for intelligent cattle health monitoring system using body area sensors and IoT","authors":"Jehangir Arshad ,&nbsp;Ahmad Irtisam ,&nbsp;Tayyaba Arif ,&nbsp;Muhammad Shahzaib Rasheed ,&nbsp;Sohaib Tahir Chauhdary ,&nbsp;Mohammad Khalid Imam Rahmani ,&nbsp;Rania Almajalid","doi":"10.1016/j.eij.2024.100488","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100488","url":null,"abstract":"<div><p>The Sustainable Development Goals (SDGs) emphasize synchronizing technology and routine life for sustainability. Food and water shortage, and exponentially increasing environmental pollution are the biggest challenges for sustainability. Livestock plays a vital role in developing countries’ economies; the most profitable businesses are breeding dairy and non-dairy products. The productivity of cattle farms is dependent on the health conditions of cattle. Identifying unhealthy cattle and providing suitable treatment is critical. Hence, deploying the Internet of Things (IoT) along with AI systems is one of the potential solutions. This cattle health monitoring system provides monitoring of cattle health to ensure the minimum human intervention. A system has been designed and developed to aid the intelligent cattle health monitoring system by using machine learning techniques. The system includes multiple sensor nodes, each having a body area sensor that is connected to the IoT platform through a controller. As a novelty, the prototype has been trained and evaluated using a federated learning technique. The system warns the owner about specific diseases such as fever, mastitis, foot and mouth disease, and ketosis. The presented results validate the proposal as it diagnoses the prescribed viral diseases precisely. We have implemented the Gaussian Naïve Bayes classifier for this multiclass problem. Considering the federated learning model, three different datasets are considered as three different clients with 70% train and 30% test data. Client 1, Client 2, and Client 3 represent the cattle farm, veterinary hospital, and veterinary respectively. The sensor nodes are placed on key points of the cattle body while each node collects physiological parameters that are further used to train the prediction system. Additionally, we have developed a user-friendly Android application for the owner to control cattle well-being. A comprehensive comparative analysis demonstrates that the proposed system outperforms existing state-of-the-art systems by showing good accuracy.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000513/pdfft?md5=cde39e08ebad3941239e2998b75740cb&pid=1-s2.0-S1110866524000513-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141422992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Cluster-based anonymity model and algorithm for 1:1 dataset with a single sensitive attribute using machine learning technique","authors":"J. Jayapradha ,&nbsp;Ghaida Muttashar Abdulsahib ,&nbsp;Osamah Ibrahim Khalaf ,&nbsp;M. Prakash ,&nbsp;Mueen Uddin ,&nbsp;Maha Abdelhaq ,&nbsp;Raed Alsaqour","doi":"10.1016/j.eij.2024.100485","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100485","url":null,"abstract":"<div><p>Privacy is a significant issue that requires consideration in all applications. Data collected from various individuals and organizations must be disclosed to the public or private parties for analysis and research purposes. The collected data are studied and analyzed digitally for the extraction of various useful patterns for decision-making research purposes. Privacy-preserving data publishing is significant as privacy violations in the patient’s data may have an adverse effect on the individual positive reputation. An efficient Cluster Based anonymity model has been proposed to anonymizes the 1:1 dataset with a single sensitive attribute through the introduction of a concept named “Semi-sensitive attribute.” Based on correlation, the attributes are categorized as quasi-identifier and semi-sensitive attributes. The k-anonymity is implemented on the quasi-identifier with the semi-sensitive attribute table and Fuzzy c-means clustering has been implemented to fix a range of values for anonymizing the semi-sensitive attributes. The disease is considered a sensitive attribute as the research work focuses on the medical dataset. The proposed model is demonstrated to resist the three privacy attacks such as, i)Identity Disclosure, ii) Attribute Disclosure, and iii) Membership Disclosure. The utility loss is calculated for each row and utility loss of each record are aggregated and considered as the total information loss for each attribute. Cluster Based anonymity model measured the utility loss for all the attributes and the average utility loss for the anonymized patient dataset is 3.78%.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000483/pdfft?md5=8857fa73ae94805ac4758e191a2acbc2&pid=1-s2.0-S1110866524000483-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Egocentric intention object prediction based on a human-like manner","authors":"Zongnan Ma ,&nbsp;Jingru Men ,&nbsp;Fuchun Zhang ,&nbsp;Zhixiong Nan","doi":"10.1016/j.eij.2024.100482","DOIUrl":"10.1016/j.eij.2024.100482","url":null,"abstract":"<div><p>This paper deals with the problem of egocentric intention object prediction, which requires a model to produce a probability map for the possible locations of human intention objects, based on an egocentric image from daily activities. Existing methods typically rely on visible indications (e.g., visual attention feature and human hand feature) to predict intention objects, assuming that intention object selection follows a bottom-up approach. However, in human decision-making on intention objects, a top-down cognitive process also occurs invisibly, analyzing object candidates’ relevance to the ongoing activity (e.g., object function’s alignment with activity goals) and the overall scene (e.g., semantic context and object distances). Based on this idea, this paper introduces a multi-modal fusion mechanism that considers both visible bottom-up cues and invisible top-down cues for predicting intention objects in a human-like manner. Additionally, this study pioneers the use of a multi-depth supervision mechanism in human intention object prediction. Our method surpasses eight baseline approaches in experiments on two public datasets, as confirmed by ablation studies validating our mechanisms’ effectiveness.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000458/pdfft?md5=5e8ef3062ff2f184b4ac29460f6331e3&pid=1-s2.0-S1110866524000458-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141234267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"AI-driven Q-learning for personalized acne genetics: Innovative approaches and potential genetic markers","authors":"Yong Chi Chua ,&nbsp;Hui Wen Nies ,&nbsp;Izyan Izzati Kamsani ,&nbsp;Haslina Hashim ,&nbsp;Yusliza Yusoff ,&nbsp;Weng Howe Chan ,&nbsp;Muhammad Akmal Remli ,&nbsp;Yong Hui Nies ,&nbsp;Mohd Saberi Mohamad","doi":"10.1016/j.eij.2024.100484","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100484","url":null,"abstract":"<div><p>Genetic markers for acne are being studied to create personalized treatments based on an individual’s genes, and the field is benefiting from the application of artificial intelligence (AI) techniques. One such AI tool, the Q-learning algorithm, is increasingly being utilized by medical researchers to delve into the genetics of acne. In contrast to previous methods, our research introduces a Q-learning model that is adaptable to diverse sample groups. This innovative approach involves preprocessing data by identifying differentially expressed genes and constructing gene-gene connectivity networks. The key advantage of using the Q-learning model lies in its ability to transform acne gene data into Markovian domains, which are essential for selecting relevant genetic markers. Performance evaluations of our Q-learning model have shown high accuracy and specificity, although there may be some sensitivity variations. Notably, this research has identified specific genes, such as CD86, AGPAT3, TMPRSS11D, DSG3, TNFRSF1B, PI3, C5AR1, and KRT16, as being acne-related through biological verification and text data mining. These findings underscore the potential of AI-driven Q-learning models to revolutionize the study of acne genetics. In conclusion, our Q-learning model offers a promising approach for the selection of acne-related genetic markers, despite minor sensitivity fluctuations. This research highlights the transformative potential of Q-learning in advancing our understanding of the genetics underlying acne, paving the way for more personalized and effective treatments in the future.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000471/pdfft?md5=2b73fe1e371a7366522efae0f6cac1bc&pid=1-s2.0-S1110866524000471-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141285974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Configuring the RegTech business model to explore implications of FinTech","authors":"Jen-Sheng Wang ,&nbsp;Yen-Tzu Chen","doi":"10.1016/j.eij.2024.100483","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100483","url":null,"abstract":"<div><p>Regulatory technology (RegTech) is a significant technology in the financial technology (FinTech) field that can assist FinTech and innovations to solve issues of complying with laws and regulations. However, RegTech is mainly composed of the finance, regulatory and emerging technology sectors, and its business model involves multiple dimensions, such as those among governments, banks and technology companies and cross-border FinTech. Therefore, RegTech startups exhibit distinctive features, and the optimum business model for their operation needs to be rapidly determined. This study uses a business model canvas (BMC) as an example to configure the elements and determinants of a RegTech start-ups and applies the Delphi technique and multiple criteria decision-making (MCDM) approaches for the analysis.</p><p>The results indicate that ‘customer relations (CR)’ and ‘key activities (KA)’ are the most significant BMC elements. Additionally, the relevant top-ranked determinants are, in their order of importance, ‘Big Data analysis’, ‘system feasibility evaluation’, ‘long-term customization’, ‘data assessment and stakeholder descriptions’, and ‘short-term projects’. In particular, business models of RegTech are the most complex in FinTech. This study concludes with business elements that can be beneficial not only for RegTech advancement but also for other emerging technologies in the FinTech.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S111086652400046X/pdfft?md5=ff431b4c3f4e17c2efee3cf549a501cb&pid=1-s2.0-S111086652400046X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141244689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"IFC: Editorial","authors":"","doi":"10.1016/S1110-8665(24)00054-9","DOIUrl":"https://doi.org/10.1016/S1110-8665(24)00054-9","url":null,"abstract":"","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000549/pdfft?md5=72b7be00224d85706bcca59a77efe397&pid=1-s2.0-S1110866524000549-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141481291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Blockchain and big data integration design for traceability and carbon footprint management in the fishery supply chain","authors":"Aslan Alwi ,&nbsp;Nugroho Adi Sasongko ,&nbsp;Suprapto ,&nbsp;Yaya Suryana ,&nbsp;Hendro Subagyo","doi":"10.1016/j.eij.2024.100481","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100481","url":null,"abstract":"<div><p>The utilization of blockchain technology in the fishing industry has been extensively studied and implemented to address issues such as illegal fishing and carbon emissions control. However, integrating blockchain with the vast amounts of data in the fishing supply chain poses significant challenges. Challenges include managing extensive data such as photos or videos for product traceability throughout their lifecycle, compounded by the growing complexity of cross-border trade and market expansion. Additionally, blockchain's storage capacity limitations present hurdles in fully accommodating and comprehensively storing detailed supply data from a complex and expanding supply chain.</p><p>While solutions like the Interplanetary File System (IPFS) have been explored for large data storage on the blockchain, this paper proposes a directly integrated blockchain solution tailored for the challenges of fishing with big data. We introduce a novel big data design that preserves blockchain's anonymity and immutability features, addressing storage limitations while maintaining the architecture's purpose. Furthermore, our proposal integrates product supply chain traceability with carbon footprint tracking, enabling comprehensive assessment based on quality, sustainability, and carbon footprint criteria.</p><p>Despite the proposed solution needing to be tested in real-life situations, we conducted rigorous testing through simulation, white-box evaluation, and complexity analysis. The results demonstrate the potential of our solution to address challenges faced in fisheries supply chains, providing valuable insights for future practical implementation and validation efforts.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000446/pdfft?md5=9093a6a29423565e7d2a24eb098a74ff&pid=1-s2.0-S1110866524000446-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141073216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Lightweight authentication protocol for connected medical IoT through privacy-preserving access","authors":"Muhammad Tanveer ,&nbsp;Samia Allaoua Chelloug ,&nbsp;Maali Alabdulhafith ,&nbsp;Ahmed A. Abd El-Latif","doi":"10.1016/j.eij.2024.100474","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100474","url":null,"abstract":"<div><p>With the rapid progress of communication technology, the Internet of Things (IoT) has emerged as an essential element in our daily lives. Given that the IoT encompasses diverse devices that often have limited resources in terms of communication, computation, and storage. Consequently, the National Institute of Standards and Technology (NIST) has standardized several lightweight cryptographic algorithms for encryption and decryption, specifically designed to meet the needs of resource-constrained IoT devices. These cryptographic algorithms, known as authenticated encryption with associated data (AEAD), offer more than just confidentiality—they also guarantee information integrity and authentication. Unlike conventional encryption algorithms like AES, which solely provide confidentiality, AEAD algorithms encompass additional functionality to achieve authenticity. This eliminates the need for separate algorithms like message authentication codes to ensure authenticity. Therefore, by leveraging the characteristics of an AEAD protocol, it is possible to develop a lightweight authentication framework to mitigate the security risks inherent in public communication channels. Therefore, in this work, we designed the lightweight authentication protocol for the smart healthcare system (BLAP-SHS) using an AEAD mechanism. In order to do this, a session key must first be created for encrypted communication. This is done via a method called mutual authentication, which verifies the legitimacy of both the user and the server. The random-or-real methodology ensures the security of the derived session key, and the Scyther tool is used to assess BLAP-SHS’ resistance to man-in-the-middle and replay attacks. Through using the technique of informal security analysis, the resilience of BLAP-SHS against denial of service, and password-guessing threats are evaluated. By juxtaposing BLAP-SHS with other prominent authentication techniques, the usefulness of BLAP-SHS is also assessed in terms of computing and communication costs. We illustrate that the BLAP-SHS requires a reduction in computation cost ranging from [70.11% to 95.21%] and a reduction in communication resources ranging from [3.85% to 9.09%], as evidenced by our comparative study.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000379/pdfft?md5=edb5ab2baf4f00488a8701aa93548240&pid=1-s2.0-S1110866524000379-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140947783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Internet of Things intrusion detection: Research and practice of NSENet and LSTM fusion models","authors":"Shaoqin Li ,&nbsp;Zhendong Wang ,&nbsp;Shuxin Yang ,&nbsp;Xiao Luo ,&nbsp;Daojing He ,&nbsp;Sammy Chan","doi":"10.1016/j.eij.2024.100476","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100476","url":null,"abstract":"<div><p>To address the problems of complex environment, limited device computational resources and limited memory resources in the existing IoT, SELSTM, an intrusion detection system composed of NSENet and LSTM fusion based on SENet, is investigated. The NSENet part of the SELSTM system is based on the squeeze-and-excitation network (SENet). The lightweight computational modules NonLocal, SKConv and inverted residuals are fused into SE blocks, and self-attention of Nonlocal is used to improve the local receptive field of feature extraction. The channel attention and spatial attention of each part of the data are strengthened by the use of SKConv To enhance the adaptive convolution ability of the model and ensure the completeness of the information, the properties of the inverted residual structure are used to ensure that the gradient of the model decreases steadily without gradient explosion or disappearance. For the problem of data imbalance, the dataset is randomly resampled using the weight resampling technique to improve the balance of the dataset to ensure that the final detection effect of the model is more effective and generalized, while the data flow is divided into two parts for processing, and the model parameters are optimized using the model gradient optimizer consisting of the optimizer Lion and the optimization function Lookahead. The model extracts the spatial and temporal features of the data through multidimensional extraction to ensure the completeness of the data feature information in multiple dimensions, thus obtaining better detection results. The results of the experiments comparing the SELSTM model with other models on the intrusion dataset show that the intrusion detection model has a higher detection precision and accuracy than the traditional deep learning intrusion detection model, which indicates that the SELSTM has better detection performance properties and better practicality and effectiveness on IoT devices.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000392/pdfft?md5=64e5793bbb60ef3e134d69edf4397375&pid=1-s2.0-S1110866524000392-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140909803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"MalRed: An innovative approach for detecting malware using the red channel analysis of color images","authors":"Syed Shakir Hameed Shah ,&nbsp;Norziana Jamil ,&nbsp;Atta ur Rehman Khan ,&nbsp;Lariyah Mohd Sidek ,&nbsp;Nazik Alturki ,&nbsp;Zuhaira Muhammad Zain","doi":"10.1016/j.eij.2024.100478","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100478","url":null,"abstract":"<div><p>Technological advancements have significantly progressed and innovated across various industries. However, these advancements have also led to an increase in cyberattacks using malware. Researchers have developed diverse techniques to detect and classify malware, including a visualization-based approach that converts suspicious files into color or grayscale images, eliminating the need for domain-specific expertise. Nonetheless, converting files to color and grayscale images may result in the loss of texture details due to noise, adversely affecting the performance of machine learning models. The aim of this study is to present to assess the texture features and noise contributions of the red, green, and blue channels in color images. We propose a novel method to enhance model performance in terms of accuracy, precision, recall, f1-score, memory utilization, and computing cost during testing and training. This study introduces an approach involves separating color channels into individual red, green, and blue datasets and using various Discrete Wavelet Transform levels to reduce dimensions and remove noise. The extracted texture characteristics are then used as input for machine learning classifiers for training and prediction. Through comprehensive evaluation, we compare the performance of grayscale images with that of the red, green, and blue datasets. The results show that grayscale images significantly lose critical textural artifacts and perform worse than the color channels. Notably, employing extra tree classifiers yielded the best results, achieving an accuracy of 98.37%, precision of 98.64%, recall of 97.60%, and an f1-score of 98.04% with the red channel of color dataset.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000410/pdfft?md5=45302b24c7f2d0c312cdb11107ac452a&pid=1-s2.0-S1110866524000410-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140894563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}