Egyptian Informatics Journal最新文献

{"title":"A private Ethereum blockchain for organ donation and transplantation based on intelligent smart contracts","authors":"V. Sitharamulu ,&nbsp;G. Sucharitha ,&nbsp;Sachi Nandan Mohanty ,&nbsp;Shaik Janbhasha ,&nbsp;D. Kothandaraman","doi":"10.1016/j.eij.2024.100542","DOIUrl":"10.1016/j.eij.2024.100542","url":null,"abstract":"<div><div>Modern organ transplant and donor procedures present numerous necessities and also obstacles related to Organ procurement, their delivery, and transplanting are all steps in the enrolment, matchmaking, and disposal process. These processes must adhere to various legal, clinical, ethical, and technical limitations. Consequently, there is a need for a comprehensive organ donation and transplantation system that ensures fairness, efficiency, and an improved experience for patients, ultimately fostering trust in the system. In this research paper, we introduce a ground-breaking solution based on the utilization of a private Ethereum blockchain. This approach revolutionizes the administration of organ gift and transplantation by establishing a copiously reorganized, sheltered, appreciable, auditable, cloistered, as well as truthful framework. Our solution is built upon the development of intelligent smart contracts and is accompanied by the presentation of six sophisticated algorithms, including their thorough enactment, trying, and endorsement procedures. To gauge the effectiveness of our projected result, we undertake an extensive evaluation encompassing privacy, security, and confidentiality analyses. Additionally, we conduct a comprehensive comparison against existing solutions to highlight the advantages and advancements offered by our system. By leveraging the potential of blockchain technology and incorporating smart contracts, our solution addresses the complexities and limitations that have plagued organ donation and transplantation systems. This research aims to enhance the overall efficacy and slide of the procedure, ultimately benefiting disease one in require of life-saving tissue relocates.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100542"},"PeriodicalIF":5.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424983","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":"Security of End-to-End medical images encryption system using trained deep learning encryption and decryption network","authors":"Saba Inam ,&nbsp;Shamsa Kanwal ,&nbsp;Anousha Anwar ,&nbsp;Noor Fatima Mirza ,&nbsp;Hessa Alfraihi","doi":"10.1016/j.eij.2024.100541","DOIUrl":"10.1016/j.eij.2024.100541","url":null,"abstract":"<div><div>The Internet of Medical Things (IoMT) links medical devices and wearable, enhancing healthcare. To secure sensitive patient data over the IoMT, encryption is vital to retain confidentiality, prevent tampering, ensure authenticity, and secure data transfer. The intricate neural network architecture of deep learning models adds a layer of complexity and non-linearity to the encryption process, rendering it highly resistant to plaintext attacks. Specifically, the Cycle_GAN network is used as the leading learning network. This work suggests deep learning-based encryption for medical images using Cycle_GAN, a Generative Adversarial Network. Cycle_GAN changes images without paired training data that improves quality and feature preservation. Unlike conventional image-to-image translation techniques, Cycle_GAN doesn’t require a dataset with corresponding input–output pairs. Traditional methods typically needs paired data to learn the mapping between input and output images. Paired data can be challenging to obtain, specifically in medical imaging where gathering and annotating data can be time-consuming, laborious and expensive. The use of Cycle_GAN overwhelms this constraint by using unpaired data, where the input and output images are not explicitly paired. This method ensures confidentiality, authenticity, and secure transfer. Cycle_GAN consists of two major components: a generator used to modify the images, and a discriminator used to distinguish between real and fake images. Further, the Binary-Cross Entropy loss function is employed to train the network for precise predictions. The experiments are carried out on skin cancer datasets. The results demonstrate high-level efficient, systematic and coherent encryption as compared with other modernized medical image encryption methods. The proposed technique offers several benefits, including efficient encryption and decryption and robustness against unauthorized access.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100541"},"PeriodicalIF":5.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323947","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":"A password-based authentication approach for edge computing architectures","authors":"Laid Kenioua ,&nbsp;Brahim Lejdel ,&nbsp;Sultan Alamri ,&nbsp;Qusai Ramadan","doi":"10.1016/j.eij.2024.100543","DOIUrl":"10.1016/j.eij.2024.100543","url":null,"abstract":"<div><div>Due to its benefits of low delay and fast response, edge computing has become an essential aid to cloud computing technology and has introduced new options for smart applications. By offloading some of the data processing tasks to the network’s edge, edge computing complements cloud computing. Nevertheless, adopting edge computing introduces several security challenges that must be addressed to ensure safety and reliability. One major concern is data privacy, as sensitive information processed at the edge must be protected from unauthorized access and breaches. Implementing strong encryption, secure authentication protocols, and regular software updates are some of the strategies necessary to enhance security in edge computing environments. In this paper, we present a robust and lightweight mutual authentication technique that is perfectly suited to low devices that can benefit from the edge computing paradigm. After the registration phase, the authentication process can be made in two rounds, the edge node and user can easily authenticate each other. Our approach is evaluated with regard to communication and computation costs realizing 982 bits and 5.955 ms respectively. analysis and experiments prove the high performance of the proposed technique.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100543"},"PeriodicalIF":5.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524001063/pdfft?md5=042060e47f5b703632c06800710f91ac&pid=1-s2.0-S1110866524001063-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315414","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":"A clustering approach for classifying scholars based on publication performance using bibliometric data","authors":"Ali Pişirgen ,&nbsp;Serhat Peker","doi":"10.1016/j.eij.2024.100537","DOIUrl":"10.1016/j.eij.2024.100537","url":null,"abstract":"<div><div>This study introduces a clustering framework that effectively evaluate scholars’ publication performance by utilizing cluster analysis and bibliometric data. In order to capture the various aspects of scholars’ publication characteristics, our proposed framework integrates four distinct features, namely “APIR” which represents Academic age, Productivity, Impact, and Recency. The proposed framework is implemented in a case study focusing on Turkish academia, utilizing a dataset comprising 13,070 scholars from 24 diverse academic divisions across 30 Turkish universities. Cluster analysis yields seven groups of scholars with diverse publishing characteristic based on APIR features and these obtained clusters are profiled as “freshmen”, “stagnant impactful mids”, “rising stars”, “stagnant and non-prolific juniors”, “stagnant impactful seniors”, “super stars”, “currently active and prolific seniors”. To enhance the cluster analysis results, additional cross analysis is performed based on scholars’ certain demographics such as affiliating institutes, divisions, academic titles, and PhD qualification. Scholars in clusters with superior publication performance are often affiliated with top-ranked universities and have academic backgrounds in the fields of Medicine, Engineering, and Natural Sciences. Practically, generated scholar segments and analysis based on these scholar profiles can serve as useful input for policy makers during having decisions about recruitment, promotion, awarding and allocation of funds.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100537"},"PeriodicalIF":5.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524001002/pdfft?md5=bbc9856409623c1d5701432705a799f7&pid=1-s2.0-S1110866524001002-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312441","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":"Machine learning-based detection of DDoS attacks on IoT devices in multi-energy systems","authors":"Hesham A. Sakr ,&nbsp;Mostafa M. Fouda ,&nbsp;Ahmed F. Ashour ,&nbsp;Ahmed Abdelhafeez ,&nbsp;Magda I. El-Afifi ,&nbsp;Mohamed Refaat Abdellah","doi":"10.1016/j.eij.2024.100540","DOIUrl":"10.1016/j.eij.2024.100540","url":null,"abstract":"<div><p>With the growing integration of IoT devices in critical infrastructure, cybersecurity threats such as Distributed Denial of Service (DDoS) attacks on Energy Hubs (EH) have become a significant concern. This study aims to address these challenges by evaluating the effectiveness of various supervised machine learning (ML) algorithms in predicting DDoS attacks targeting EH systems through IoT devices. Using the CICDDOS2019 and KDD-CUP datasets, a comprehensive analysis was conducted on several classifiers, including Decision Tree (DT), Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest. The results highlight Gradient Boosting as the most effective model, particularly for the CICDDOS2019 dataset, demonstrating superior accuracy and predictive capability. Additionally, hybrid models combining Gradient Boosting with SVM or DT showed strong performance, though with varying precision and recall. This study provides valuable insights into the selection and tailoring of ML models for specific security challenges, emphasizing the need for ongoing research to enhance the resilience of EH systems and IoT devices against evolving DDoS threats.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100540"},"PeriodicalIF":5.0,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524001038/pdfft?md5=c152411211f2d1ecb3239e35c09f18be&pid=1-s2.0-S1110866524001038-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274745","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":"A novel optimal deep learning approach for designing intrusion detection system in wireless sensor networks","authors":"K. Sedhuramalingam ,&nbsp;N. Saravanakumar","doi":"10.1016/j.eij.2024.100522","DOIUrl":"10.1016/j.eij.2024.100522","url":null,"abstract":"<div><p>A wireless sensor network contains many nodes to collect and transfer data to a primary location. However, wireless sensor networks have several security issues because of their resource-constrained nodes, deployment tactics, and communication channels. As a result, detecting intrusions is crucial for strengthening the safety of wireless sensor networks. Naturally, any communication network will need the services provided by a network intrusion detection system. Despite their everyday use in intrusion detection systems, the efficacy of machine learning (ML) approaches needs to be improved for handling asymmetrical attacks. This article proposes an intrusion detection system based on an Improved deep neural network (IDNN) to solve this issue and enhance performance. Using the global search strategy of the coyote optimization algorithm (COA-GS) on the KDDCup 99 and WSN-DS datasets, the following hyperparameter selection techniques are used to determine network topologies and the optimal network parameters for DNNs. The most efficient algorithm for detecting future cyberattacks can be chosen by conducting such research. Extensive studies comparing COA-GS-IDNNs and other standard machine learning classifications on a large number of openly accessible malware benchmark datasets are presented. Extensive experimental testing demonstrates that DNNs outperform conventional machine learning classifiers at real-time monitoring network activity and host-level events to detect and prevent intrusions.The experimental outcomes demonstrate that the suggested COA-GS-IDNN model increases the accuracy ratio of 95 %, the precision ratio of 94 %, recall ratio of 96 %, F1-score ratio of 95 %, ROC AUC ratio 98 %, detection time of 1.0068754, and delay of 0.8016 ms compared to other existing models.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"27 ","pages":"Article 100522"},"PeriodicalIF":5.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000859/pdfft?md5=786d303351ef6e91c9885885ffda1542&pid=1-s2.0-S1110866524000859-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274874","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":"Classification and monitoring of arm exercises using machine learning and wrist-worn band","authors":"Aamer Bilal Asghar ,&nbsp;Maham Majeed ,&nbsp;Abdullah Taseer ,&nbsp;Muhammad Burhan Khan ,&nbsp;Khazina Naveed ,&nbsp;Mujtaba Hussain Jaffery ,&nbsp;Ahmed Sayed Mohammed Metwally ,&nbsp;Krzysztof Ejsmont ,&nbsp;Mirosław Nejman","doi":"10.1016/j.eij.2024.100534","DOIUrl":"10.1016/j.eij.2024.100534","url":null,"abstract":"<div><p>Exercise is essential for a healthy lifestyle, thus it is important to consider how to keep proper posture when performing arm exercises at home. This work uses wrist-worn bands with the MPU6050 sensor to address these issues, which collects motion data using acceleration measurements. The individuals in the dataset are completing a variety of activities at varying ranges of motion. Machine learning-based classification methods are then applied after the pre-processing and feature extraction of the gathered data. An App prototype integrated with a WiFi module and Cloud infrastructure is created to enable real-time data collecting and storage. The Arduino IDE is used to send the collected data to the ThingSpeak platform, where it is subsequently sent to MATLAB for additional analysis. The studied data is then returned to ThingSpeak, where the program displays the findings. This approach reduces the risk of injuries caused by bad posture by enabling people to continue regular workouts at home without requiring a personal trainer or a particular environment. The findings of this work shed important light on the performance of Boosted Trees, Quadratic SVM, Subspace KNN, and Fine KNN algorithms for arm exercises employing a wrist-worn band with an MPU6050 sensor. The Fine KNN has the highest accuracy of 91.3% among all implemented algorithms.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"27 ","pages":"Article 100534"},"PeriodicalIF":5.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000975/pdfft?md5=f84bbdb09921e3c8c928c9b8d117237b&pid=1-s2.0-S1110866524000975-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151913","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":"Machine learning RNNs, SVM and NN Algorithm for Massive-MIMO-OTFS 6G Waveform with Rician and Rayleigh channel","authors":"Arun Kumar ,&nbsp;Nishant Gaur ,&nbsp;Aziz Nanthaamornphong","doi":"10.1016/j.eij.2024.100531","DOIUrl":"10.1016/j.eij.2024.100531","url":null,"abstract":"<div><p>Multiple Input and Output-Orthogonal Time–Frequency Selective (MIMO-OTFS) is considered one of the leading candidates for the beyond fifth generation (B5G) radio framework. The signal detection process is complex due to the large number of antennas, which also increases the framework’s latency. Signal detection algorithms such as Recurrent Neural Networks (RNNs), Neural Networks (NNs), Support Vector Machines (SVMs), Minimum Mean Square Error (MMSE), Maximum Likelihood Detection (MLD), Expectation-Maximization (EM), and Zero-Forcing Equalization (ZFE) are analyzed for Rayleigh and Rician channels. Currently available methods involve intricate identification and receivers with lower spectral efficiency. Experimental results indicate that RNNs, NNs, and SVM detectors, which have lower complexity, are recommended to improve the bit error rate (BER) and power spectral density (PSD) of the MIMO-OTFS system. It is also noted that RNNs offer diversity in received data, achieving a significant gain of 5 dB to 7 dB compared to existing OTFS systems across different MIMO frameworks. Furthermore, the utilization of machine learning algorithms significantly obtained a gain of −305 and −330 (RNNs) for the Rayleigh and Rician channels, respectively. These findings underscore the benefits of integrating sophisticated detection methods in B5G communication channels, indicating a valuable direction for future research and advancements in this area.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"27 ","pages":"Article 100531"},"PeriodicalIF":5.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S111086652400094X/pdfft?md5=a8a2955bb15f0614c24d11f98ebfd11d&pid=1-s2.0-S111086652400094X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151911","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":"Implementation and test of a Device-Free localization system with a modified desync network protocol and a weighted k-nearest neighbor algorithm","authors":"Yoschanin Sasiwat,&nbsp;Dujdow Buranapanichkit,&nbsp;Apidet Booranawong","doi":"10.1016/j.eij.2024.100532","DOIUrl":"10.1016/j.eij.2024.100532","url":null,"abstract":"<div><p>A device-free localization system is a technology for tracking targets or individuals without requiring them to carry any electronic devices. The system works by monitoring and processing changes in the received signal strength to detect changes in the environment. However, due to unreliable wireless communications and radio-based tracking solutions, an efficient system concerning both wireless communication and tracking performance should be developed. This paper presents a study of the 2.4 GHz IEEE 802.15.4 device-free localization system, focusing on the effectiveness of wireless network protocols and the accuracy of localization algorithms. The novelty and contribution of our work is that we develop a modified desync protocol for network synchronization and the weighted k-nearest neighbor algorithm for location tracking. The study provides both simulation and experimental evaluations, considering hardware configurations such as the CC2538 + CC2592 device. Results demonstrate that the modified desync protocol can effectively operate in real-world environments. The network’s performance is evaluated through the packet delivery ratios for different network sizes and the convergence time, which refers to the ability to restore synchronization among network nodes. In our experiment case, the packet delivery ratio and the convergence time for a twenty-node network size are 97.98 % and 6.976 s, respectively. In addition, the weighted k-nearest neighbor algorithm with an additional solution provides a high estimation accuracy of 99.93 % as accessed from various fixed human locations. Results also indicate that our algorithm can track the locations of a movement person, achieving an average accuracy of 85.75 % for different movement patterns. Finally, we suggest that the effect of new generative artificial intelligence approaches in this field should be investigated.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"27 ","pages":"Article 100532"},"PeriodicalIF":5.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000951/pdfft?md5=184f9caa50761519e2eeaac587efbe0a&pid=1-s2.0-S1110866524000951-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121933","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":"Deep hybrid approach with sequential feature extraction and classification for robust malware detection","authors":"Swapnil Singh ,&nbsp;Deepa Krishnan ,&nbsp;Vidhi Vazirani ,&nbsp;Vinayakumar Ravi ,&nbsp;Suliman A. Alsuhibany","doi":"10.1016/j.eij.2024.100539","DOIUrl":"10.1016/j.eij.2024.100539","url":null,"abstract":"<div><p>Malware attacks have escalated significantly with an increase in the number of internet users and connected devices. With the increasingly different types of malware released by hackers, designing new and competitive techniques to detect advanced malware is essential. In the proposed research, we have developed a multi-level feature extraction technique using deep learning architectures and a classification model to classify malware families. The essential features from the malware images are extracted using the Gated Recurrent Unit in the first step, which are further fed to a Convolutional Neural Network model for extracting the final feature vector. The multi-level feature selection is followed by classification into various malware families using Cost-sensitive Boot Strapped Weighted Random Forest (CSBW-RF). The proposed approach gave promising results of 99.58 % accuracy in distinguishing the 25 different malware families on the Mallmg dataset. This hybrid model gave significantly better performance scores for classifying visually similar malware families. The generalizability of the proposed model is benchmarked with the popular Microsoft Big 2015 dataset and has achieved comparatively higher performance scores than many existing models. This benchmarking demonstrates the robustness and scalability of our approach. The use of cost-sensitive learning and bootstrapping techniques also contributed to the model’s ability to generalize well to new and unseen data. These enhancements ensure that our model can be effectively applied in diverse real-world scenarios, maintaining high performance across different environments and malware types. This research can contribute to detecting malware attacks and can be integrated in threat monitoring systems. The successful application of this hybrid model indicates its potential for deployment in real-world cybersecurity environments, providing a strong defense against evolving malware threats.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"27 ","pages":"Article 100539"},"PeriodicalIF":5.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524001026/pdfft?md5=9a6497ba22f60fe6be5116413f7890b0&pid=1-s2.0-S1110866524001026-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142229958","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}