Egyptian Informatics Journal最新文献

{"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":null,"pages":null},"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":null,"pages":null},"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":"Deepfake detection: Enhancing performance with spatiotemporal texture and deep learning feature fusion","authors":"Abdelwahab Almestekawy ,&nbsp;Hala H. Zayed ,&nbsp;Ahmed Taha","doi":"10.1016/j.eij.2024.100535","DOIUrl":"10.1016/j.eij.2024.100535","url":null,"abstract":"<div><p>Deepfakes bring critical ethical issues about consent, authenticity, and the manipulation of digital content. Identifying Deepfake videos is one step towards fighting their malicious uses. While the previous works introduced accurate methods for Deepfake detection, the stability of the proposed methods is rarely discussed. The problem statement of this paper is to build a stable model for Deepfake detection. The results of the model should be reproducible. In other words, if other researchers repeat the same experiments, the results should not differ. The proposed technique combines multiple spatiotemporal textures and deep learning-based features. An enhanced 3D Convolutional Neural Network, which contains a spatiotemporal attention layer, is utilized in a Siamese architecture. Various analyses are carried out on the control parameters, feature importance, and reproducibility of results. Our technique is tested on four datasets: Celeb-DF, FaceForensics++, DeepfakeTIMIT, and FaceShifter. The results demonstrate that a Siamese architecture can improve the accuracy of 3D Convolutional Neural Networks by 7.9 % and reduce the standard deviation of accuracy to 0.016, which indicates reproducible results. Furthermore, adding texture features enhances accuracy by up to 91.96 %. The final model can achieve an Area Under Curve (AUC) up to 97.51 % and 95.44 % in same-dataset and cross-dataset scenarios, respectively. The main contributions of this work are the enhancement of model stability and the assurance of result repeatability, ensuring consistent results with high accuracy.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000987/pdfft?md5=df12b9858a677adbb12325d75e4f6a78&pid=1-s2.0-S1110866524000987-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151912","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":null,"pages":null},"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}

{"title":"Enhancing brain tumor segmentation in MRI images: A hybrid approach using UNet, attention mechanisms, and transformers","authors":"Thien B. Nguyen-Tat ,&nbsp;Thien-Qua T. Nguyen ,&nbsp;Hieu-Nghia Nguyen ,&nbsp;Vuong M. Ngo","doi":"10.1016/j.eij.2024.100528","DOIUrl":"10.1016/j.eij.2024.100528","url":null,"abstract":"<div><p>Accurate brain tumor segmentation in MRI images is crucial for effective treatment planning and monitoring. Traditional methods often encounter challenges due to the complexity and variability of tumor shapes and textures. Consequently, there is a growing need for automated solutions to assist healthcare professionals in segmentation tasks, improving efficiency and reducing workload. This study introduces an innovative method for accurately segmenting brain tumors in MRI images by employing a refined 3D UNet model integrated with a Transformer. The goal is to leverage self-attention mechanisms to enhance segmentation capabilities. The proposed model combines Contextual Transformer (CoT) and Double Attention (DA) architectures. CoT is extended to a 3D format and integrated with the baseline model to exploit intricate contextual details in MRI images. DA blocks in skip connections aggregate and distribute long-range features, emphasizing inter-dependencies within an expanded spatial scope. Experimental results demonstrate superior segmentation performance compared to current state-of-the-art methods. With its ability to accurately segment and delineate tumors in 3D, our segmentation model promises to be a powerful tool for medical image processing and performance optimization, saving time for healthcare professionals and healthcare systems.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000914/pdfft?md5=2097a3a3d4a288323a47198f8f29bd1c&pid=1-s2.0-S1110866524000914-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096569","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":"DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment","authors":"Goda Srinivasa Rao ,&nbsp;P. Santosh Kumar Patra ,&nbsp;V.A. Narayana ,&nbsp;Avala Raji Reddy ,&nbsp;G.N.V. Vibhav Reddy ,&nbsp;D. Eshwar","doi":"10.1016/j.eij.2024.100526","DOIUrl":"10.1016/j.eij.2024.100526","url":null,"abstract":"<div><p>The Internet of Things (IoT) network infrastructures are becoming more susceptible to distributed denial of service (DDoS) attacks because of the proliferation of IoT devices. Detecting and predicting such attacks in this complex and dynamic environment requires specialized techniques. This study presents an approach to detecting and predicting DDoS attacks from a realistic multidimensional dataset specifically tailored to IoT network environments, named DDoSNet. At the beginning of the data preprocessing phase, the dataset must be cleaned up, missing values must be handled, and the data needs to be transformed into an acceptable format for analysis. Several preprocessing approaches, including data-cleaning algorithms and imputation methods, are used to improve the accuracy and dependability of the data. Following this, feature selection uses the African Buffalo Optimization with Decision Tree (ABO-DT) method. This nature-inspired metaheuristic algorithm imitates the behaviour of African buffalos to determine which traits are the most important. By integrating ABO with the decision tree, a subset of features is selected that maximizes the discrimination between regular network traffic and DDoS attacks. After feature selection, an echo-state network (ESN) classifier is employed for detection and prediction. A recurrent neural network (RNN) that has shown potential for managing time-series data is known as an ESN. The ESN classifier utilizes the selected features to learn the underlying patterns and dynamics of network traffic, enabling accurate identification of DDoS attacks. Based on the simulations, the proposed DDOSNet had an accuracy of 98.98 %, a sensitivity of 98.62 %, a specificity of 98.85 %, an F-measure of 98.86 %, a precision of 98.27 %, an MCC of 98.95 %, a Dice coefficient of 98.04 %, and a Jaccard coefficient of 98.09 %, which are better than the current best methods.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000896/pdfft?md5=a4fdb339654ba9b0125e04ea60ed970b&pid=1-s2.0-S1110866524000896-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151958","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":"Enhanced threat intelligence framework for advanced cybersecurity resilience","authors":"Moutaz Alazab ,&nbsp;Ruba Abu Khurma ,&nbsp;Maribel García-Arenas ,&nbsp;Vansh Jatana ,&nbsp;Ali Baydoun ,&nbsp;Robertas Damaševičius","doi":"10.1016/j.eij.2024.100521","DOIUrl":"10.1016/j.eij.2024.100521","url":null,"abstract":"<div><p>The increasing severity of cyber-attacks against organizations emphasizes the necessity for efficient threat intelligence. This article presents a novel multi-layered architecture for threat intelligence that integrates diverse data streams, including corporate network logs, open-source intelligence, and dark web monitoring, to offer a comprehensive overview of the cybersecurity threat landscape. Our approach, distinct from previous studies, uniquely integrates these varied features into the machine-learning algorithms (XGBoost, Gradient Boosting, LightGBM, Extra Trees, Random Forest, Decision Tree, K-Nearest Neighbor, Gaussian Naive Bayes, Support Vector Machine, Linear Discriminant Analysis, Logistic Regression, ridge Classifier, AdaBoost and Quadratic Discriminant Analysis) using various feature selection algorithms (information gain, correlation coefficient, chi-square, fisher score, forward wrapper, backward wrapper, Ridge classifier) to enhance real-time threat detection and mitigation. The practical LITNET-2020 dataset was utilized to evaluate the proposed architecture. Extensive testing against real-world cyber-attacks, including malware and phishing, demonstrated the robustness of the architecture, achieving exceptional results. Specifically, XGBoost demonstrated the highest performance with a detection accuracy of 99.98%, precision of 99.97%, and recall of 99.96%, Significantly surpassing traditional methods. Gradient Boosting and LightGBM also exhibited excellent performance, with accuracy, precision, and recall values of 99.97%. Our findings underscore the effectiveness of our architecture in significantly improving an organization’s capability to identify and counteract online threats in real-time. By developing a comprehensive threat intelligence framework, this study advances the field of cybersecurity, providing a robust tool for enhancing organizational resilience against cyber-attacks.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000847/pdfft?md5=13cf1f334977f99a734fe637ad1d8f35&pid=1-s2.0-S1110866524000847-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161831","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":"Vision-based initial localization of AGV and path planning with PO-JPS algorithm","authors":"Zheng Wang ,&nbsp;Hangyao Tu ,&nbsp;Sixian Chan ,&nbsp;Chengkan Huang ,&nbsp;Yanwei Zhao","doi":"10.1016/j.eij.2024.100527","DOIUrl":"10.1016/j.eij.2024.100527","url":null,"abstract":"<div><p>In recent years, robot path planning has gained high attention. The traditional adaptive Monte Carlo localization (AMCL) has such problems as limitations in global localization, and incomplete path and time-consuming problem in path planning due to too much calculation of meaningless nodes by the jump point search (JPS) algorithm. In view of the above problems, this paper proposed a method for vision-based initial localization of automated guided vehicle (AGV) and path planning with (pruning optimization) PO-JPS algorithm. The core contents include: vision-based AMCL localization module and improved JPS algorithm based on pruning optimization. Firstly, Oriented FAST and Rotated BRIEF (ORB) features are extracted from the images collected by vision, and coordinates are localized with the features, coupled with the initial map by laser SLAM, to construct a bag-of-words (BoW) library of features. The key frame most similar to the current one is obtained by comparing the similarity between the current and historical frames in the BoW library. The Euler transformation between these two frames is calculated, to carry out pose estimation. This pose, as an initial value, is provided to the AMCL for particle iteration. Secondly, in the path planning stage, an improved JPS algorithm based on pruning optimization is proposed, and a strategy that the repeated intermediate inflection points in the complemented path after pathfinding are deleted is designed. Therefore, while a complete path is obtained, the calculation workload and memory consumption for meaningless nodes during node extension are reduced successfully, and the efficiency of the pathfinding algorithm is raised. Finally, verification of the method proposed in this paper is completed through a large number of simulations and physical experiments, which saved 17.7% of the time compared to the original JPS algorithm and 279.6% to the A* algorithm.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000902/pdfft?md5=d39015f935bd8a59d4c7e6ca09c22d46&pid=1-s2.0-S1110866524000902-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151916","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)00108-7","DOIUrl":"10.1016/S1110-8665(24)00108-7","url":null,"abstract":"","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524001087/pdfft?md5=c56b3f146f260bc5f5e3587c64fef5d1&pid=1-s2.0-S1110866524001087-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314358","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":"Recognizing human activities with the use of Convolutional Block Attention Module","authors":"Mohammed Zakariah ,&nbsp;Abeer Alnuaim","doi":"10.1016/j.eij.2024.100536","DOIUrl":"10.1016/j.eij.2024.100536","url":null,"abstract":"<div><p>Human Activity Recognition (HAR) is crucial for the advancement of applications in smart environments, communication, IoT, security, and healthcare monitoring. Convolutional neural networks (CNNs) have made substantial contributions to human activity recognition (HAR). However, they frequently encounter difficulties in accurately discerning intricate human actions in real-time situations. This study aims to fill a significant research gap by incorporating the Convolutional Block Attention Module (CBAM) into CNN architectures. The goal is to improve the extraction of features from video sequences. The CBAM boosts the performance of the network by selectively prioritizing significant spatial and channel-wise data, resulting in improved detection of subtle activity patterns and increased stability in categorization. CBAM’s attention mechanism directly focuses and amplifies essential characteristics, which sets it apart from typical CNNs that lack a refined focus mechanism. This unique approach results in improved performance in behavior identification tests. The proposed CBAM-enhanced model has been extensively tested on benchmark datasets, yielding an accuracy of 94.23% on the HMDB51 dataset. It also achieved competitive results of 83.4% and 88.9% on the UCF-101 and UCF-50 datasets, respectively. However, there is still a lack of study in comprehending how CBAM adjusts to different CNN architectures and its suitability in varied HAR situations beyond controlled datasets. In future studies, it is imperative for researchers to investigate the integration of CBAM with other CNN frameworks, assess its efficacy in practical scenarios, and explore multi-modal sensor fusion techniques to enhance its reliability and utility. This study showcases the ability of CBAM to enhance HAR capabilities and also paves the way for future research to improve activity identification systems for wider and more practical uses.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000999/pdfft?md5=ecc0aedcf9be8ae7e087777abd06f4e1&pid=1-s2.0-S1110866524000999-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151917","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}