Multimedia Tools and Applications最新文献_第4页

Principal component fusion based unexposed biological feature enhancement of fundus images 基于主成分融合的眼底图像未曝光生物特征增强技术

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-14 DOI: 10.1007/s11042-024-20110-4

Neha Singh, Ashish Kumar Bhandari

{"title":"Principal component fusion based unexposed biological feature enhancement of fundus images","authors":"Neha Singh, Ashish Kumar Bhandari","doi":"10.1007/s11042-024-20110-4","DOIUrl":"https://doi.org/10.1007/s11042-024-20110-4","url":null,"abstract":"In the field of ophthalmology, digital images play an important role for automatic detection of various kind of eye diseases. Digital images in the field image enhancement are the first stage to assisting ophthalmologist for diagnosis. As a result, various algorithms, and methods for the enhancement of retinal images have been developed, which may face obstacles that are common in augmentation processes, such as false edges and weak illuminated that obscure image particulars. To eliminate such issues, this paper projected a novel framework for unexposed retinal image. The proposed paper uses multiscale Gaussian function for estimation of illumination layer from unexposed color retinal image and then it is corrected by gamma method. Further to this, the principal component analysis (PCA) is utilized here to generate fused enhance result for unexposed retinal images. Then, contrast limited technique is employed here for further edge and contextual details improvement. When compared to several enhancement-based state-of-the-art procedures, experimental results show that the suggested method produces results with good contrast and brightness. The significance of the proposed method that this method may help ophthalmologists screen for unexposed retinal illnesses more efficiently and build better automated image analysis for healthcare diagnosis.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":"11 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personal mark density-based high-performance Optical Mark Recognition (OMR) system using K-means clustering algorithm 使用 K-means 聚类算法的基于个人标记密度的高性能光学标记识别（OMR）系统

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-14 DOI: 10.1007/s11042-024-20218-7

Yasin Sancar, Ugur Yavuz, Isil Karabey Aksakalli

{"title":"Personal mark density-based high-performance Optical Mark Recognition (OMR) system using K-means clustering algorithm","authors":"Yasin Sancar, Ugur Yavuz, Isil Karabey Aksakalli","doi":"10.1007/s11042-024-20218-7","DOIUrl":"https://doi.org/10.1007/s11042-024-20218-7","url":null,"abstract":"To evaluate multiple choice question tests, optical forms are commonly used for large-scale exams and these forms are read by the OMR (Optical Mark Recognition) scanners. However, OMR scanners often misinterpret marks that have not been fully erased, which can lead to incorrect readings. To overcome that shortcoming and reduce the time and labor lost in the assessment process, we developed a novel system based on the density of each individual’s markings, providing a more personalized and accurate approach. Instead of reading according to a specific optical form template, a dynamic and flexible structure was generated where users can create own templates and obtain the model that reads according to that template. We also optimized certain aspects of the system for efficiency, such as image memory transfer and QR code reading. These optimizations significantly increase the performance of the OMR scanners. One of the key issues addressed is inaccurate reading of OMR scanners when a student doesn’t fully erase their markings or when markings are faint. After the scanning process, the proposed approach uses a K-means clustering algorithm to classify different density markings. This technique identifies each student’s personal marking density, enabling a more accurate interpretation of their responses. According to the experimental results, we performed 97.7% improvement compared to the misread optics scanned by the conventional OMR devices. In tests performed on 265.816 optical forms, we obtained an accuracy rate of 99.98% and a reading time of 0.12 seconds per optical form.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":"4 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Speed-enhanced convolutional neural networks for COVID-19 classification using X-rays 利用 X 射线对 COVID-19 进行分类的速度增强型卷积神经网络

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-14 DOI: 10.1007/s11042-024-20153-7

Palwinder Kaur, Amandeep Kaur

{"title":"Speed-enhanced convolutional neural networks for COVID-19 classification using X-rays","authors":"Palwinder Kaur, Amandeep Kaur","doi":"10.1007/s11042-024-20153-7","DOIUrl":"https://doi.org/10.1007/s11042-024-20153-7","url":null,"abstract":"COVID-19 emerged as a pandemic in December 2019. This virus targets the pulmonary systems of humans. Therefore, chest radiographic imaging is required to monitor effect of the virus, prevent the spread and decrease the mortality rate. Imaging-based testing leads to a high burden on the radiologist manually screening the images. To make the imaging-based method an efficient diagnosis tool, screening automation with minimum human interference is a necessity. It opens numerous challenges for scientists and researchers to develop automatic diagnostic tools for COVID-19 detection. In this paper, we present two speed-enhanced convolutional neural networks (SECNNs) to automatically detect COVID-19 among the X-rays of COVID-19, pneumonia and healthy subjects. For 2-class classification (2CC) and 3-class classification (3CC), we named the models SECNN-2CC and SECNN-3CC respectively. The scope of this work is to highlight the significance and potential of CNN models built from scratch in COVID-19 identification. We conduct six experiments using six different balanced and imbalanced kinds of datasets. In the datasets, All X-rays are from different patients therefore it was more challenging for us to design the models which extract abstract features from a highly variable dataset. Experimental results show that the proposed models exhibit exemplary performance. The highest accuracy for 2CC (COVID-19 vs Pneumonia) is 99.92%. For 3CC (COVID-19 vs Normal vs Pneumonia), the highest accuracy achieved is 99.51%. We believe that this study will be of great importance in diagnosing COVID-19 and also provide a deeper analysis to discriminate among pneumonia, COVID-19 patients and healthy subjects using X-rays.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":"2 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Is a poster a strong signal of film quality? evaluating the predictive power of visual elements using deep learning 利用深度学习评估视觉元素的预测能力？

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-14 DOI: 10.1007/s11042-024-20174-2

Thaís Luiza Donega e Souza, Caetano Mazzoni Ranieri, Anand Panangadan, Jó Ueyama, Marislei Nishijima

{"title":"Is a poster a strong signal of film quality? evaluating the predictive power of visual elements using deep learning","authors":"Thaís Luiza Donega e Souza, Caetano Mazzoni Ranieri, Anand Panangadan, Jó Ueyama, Marislei Nishijima","doi":"10.1007/s11042-024-20174-2","DOIUrl":"https://doi.org/10.1007/s11042-024-20174-2","url":null,"abstract":"A film is considered an experience good, as its quality is only revealed after consumption. This situation creates information asymmetry before consumption, prompting producers, who are aware of their film’s quality, to search for methods to signal this. Economic literature specifies that a signal to disclose a product’s quality must be strong, meaning only producers of good-quality films can effectively utilize such a signal. However, a poster represents the most economical signal, and all producers, regardless of film quality, have access to this option. To study whether a poster can signal film quality, we first apply a low-dimensional representation of poster images and cluster them to identify quality-related patterns. We then perform a supervised classification of films into economically successful and unsuccessful categories using a deep neural network. This is based on the hypothesis that higher quality films tend to sell more tickets and that all producers invest in the highest quality poster services. The results demonstrate that a film’s quality can indeed be predicted from its poster, reinforcing its effectiveness as a strong signal. Despite the proliferation of advanced visual media technologies, a simple yet innovative poster remains an effective and appealing tool for signaling film information. Notably, posters can classify a film’s economic success comparably to trailers but with significantly lower processing costs.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":"119 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Manifold grasshopper optimization based extremely disruptive vision transformer model for automatic heart disease detection in raw ECG signals 基于极具破坏性的视觉变换器模型的歧面蚂蚱优化技术，用于在原始心电信号中自动检测心脏病

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-14 DOI: 10.1007/s11042-024-20113-1

Avinash L. Golande, Pavankumar T.

{"title":"Manifold grasshopper optimization based extremely disruptive vision transformer model for automatic heart disease detection in raw ECG signals","authors":"Avinash L. Golande, Pavankumar T.","doi":"10.1007/s11042-024-20113-1","DOIUrl":"https://doi.org/10.1007/s11042-024-20113-1","url":null,"abstract":"Automated detection of cardiovascular diseases based on heartbeats is a difficult and demanding task in signal processing because the routine analysis of the patient’s cardiac arrhythmia is crucial to reducing the mortality rate. Detecting and preventing these deaths requires long-term monitoring and manual examination of electrocardiogram (ECG) signals, which takes a lot of time. This article uses an optimized Vision Transformer technique to effectively detect heart disease. The four key processes are pre-processing input data, feature extraction from pre-processed data, and optimal feature selection and classification to detect heart disease. In the pre-processing phase, single-channel adaptive blind source separation is used for artifact removal and empirical mode decomposition for noise reduction of the ECG signal. After pre-processing, the ECG signal is fed into the Enhanced Pan-Tompkins algorithm (EPTA) and the Hybrid Gabor-Walsh-Hadamard transform (HGWHT) for feature extraction. The extracted feature is selected using a Manifold Grasshopper Optimization algorithm (MGOA). Finally, an Optimized Vision Transformer (OVT) detects heart disease. The experiment is carried out on PTB diagnostic ECG and PTB-XL database, a publicly accessible research datasets. The experiment obtained the following values: accuracy 99.9%, sensitivity 98%, F1 score 99.9%, specificity 90%, processing time 13.254 s, AUC 99.9% and MCC 91% using PTB diagnostic ECG. On the other hand, the proposed method has obtained an accuracy of 99.57%, f1-score of 99.17% and AUC of 99% using PTB-XL dataset. Thus, the overall findings prove that the proposed method outperforms the existing methodology.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":"21 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A cross-domain person re-identification algorithm based on distribution-consistency and multi-label collaborative learning 基于分布一致性和多标签协作学习的跨域人员再识别算法

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-13 DOI: 10.1007/s11042-024-20142-w

Baohua Zhang, Chen Hao, Xiaoqi Lv, Yu Gu, Yueming Wang, Xin Liu, Yan Ren, Jianjun Li

{"title":"A cross-domain person re-identification algorithm based on distribution-consistency and multi-label collaborative learning","authors":"Baohua Zhang, Chen Hao, Xiaoqi Lv, Yu Gu, Yueming Wang, Xin Liu, Yan Ren, Jianjun Li","doi":"10.1007/s11042-024-20142-w","DOIUrl":"https://doi.org/10.1007/s11042-024-20142-w","url":null,"abstract":"To decrease domain shift in cross-domain person re-identification, existing methods generate pseudo labels for training models, however, the inherent distribution between source domain data and the hard quantization loss is ignored. Therefore, a cross-domain person re-identification method based on distribution consistency and multi-label collaborative learning is proposed. Firstly, a soft binary cross-entropy loss function is constructed to constrain the inter-sample relationship of cross-domain transformation, which can ensure the consistency of appearance features and sample distribution, and achieving feature cross-domain alignment. On this basis, in order to suppress the noise of hard pseudo labels, a multi-label collaborative learning network is constructed. The soft pseudo labels are generated by using the collaborative foreground features and global features to guide the network training, making the model adapt to the target domain. The experimental results show that the proposed method has better performance than that of recent representative methods.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":"29 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Co-clustering method for cold start issue in collaborative filtering movie recommender system 协同过滤电影推荐系统中冷启动问题的聚类方法

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-13 DOI: 10.1007/s11042-024-20103-3

Ensieh AbbasiRad, Mohammad Reza Keyvanpour, Nasim Tohidi

{"title":"Co-clustering method for cold start issue in collaborative filtering movie recommender system","authors":"Ensieh AbbasiRad, Mohammad Reza Keyvanpour, Nasim Tohidi","doi":"10.1007/s11042-024-20103-3","DOIUrl":"https://doi.org/10.1007/s11042-024-20103-3","url":null,"abstract":"Recommender systems play an essential role in decision-making in the information age by reducing information overload via retrieving the most relevant information in various applications. They also present great opportunities and challenges for business, government, education, and other fields. The cold start problem is a significant issue in these systems. If recommender systems fail to provide satisfactory personalized recommendations for new users, the user’s trust can easily be lost. Hence, in this paper, using co-clustering and utilizing user demographic information and the behavioral history of users, a solution to this critical issue for recommending movies is introduced. In the proposed method, in addition to dealing with the problem of relative cold start, the problem of absolute cold start is also addressed. The proposed method was evaluated via two RMSE and MAE criteria, which accordingly has achieved 0.85 and 0.49 on the Movielens dataset and 1.05 and 0.6 on the EachMovie dataset, respectively, according to the number of comments that Cold Start users have registered. Moreover, it achieved 0.9 and 0.55 on the Movielens dataset and 1.42 and 0.89 on the EachMovie dataset, respectively, according to the number of registered comments for the cold start items.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":"13 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Acoustic data acquisition and integration for semantic organization of sentimental data and analysis in a PWSN 采集和整合声学数据，以便在 PWSN 中对情感数据进行语义组织和分析

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-13 DOI: 10.1007/s11042-024-20229-4

Sushovan Das, Uttam Kr. Mondal

{"title":"Acoustic data acquisition and integration for semantic organization of sentimental data and analysis in a PWSN","authors":"Sushovan Das, Uttam Kr. Mondal","doi":"10.1007/s11042-024-20229-4","DOIUrl":"https://doi.org/10.1007/s11042-024-20229-4","url":null,"abstract":"In an acoustic pervasive wireless sensor network (PWSN), the BASE station plays a vital role in gathering and integrating acoustic sensor data from various nodes, including end and router devices tracking time-driven events. The semantic BASE station is crucial in the IoT landscape as it consolidates data from these networks, enabling thorough sentiment analysis of acoustic signals and yielding insights across domains. A semantic processor at the BASE station is essential for an energy-efficient and intelligent PWSN, managing data collection, integration, signal feature extraction, and publication for model training and sentiment analysis. This paper introduces a novel approach to designing a semantic BASE station, focusing on ontology generation, evaluation, and updates to bolster pervasive wireless sensors in capturing and depicting events and time through an ontological framework. The study addresses challenges in efficiently gathering, integrating, and processing acoustic data from pervasive nodes, proposing a semantic processor at the BASE station to enhance feature extraction and metadata publication. The semantic organization of feature-extracted labeled metadata enables the analysis of comprehensive machine learning (ML) applications such as sentiment analysis, type detection, and environment detection by generating confusion matrix. Evaluation includes performance metrics (NEEN, LSNS, BDAS) as well as accuracy, precision, sensitivity, and specificity for sentimental data analysis to validate the proposed technique’s efficacy.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":"15 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new four-tier technique for efficient multiple images encryption 高效多图像加密的新四层技术

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-13 DOI: 10.1007/s11042-024-20125-x

Khalid M. Hosny, Sara T. Kamal

{"title":"A new four-tier technique for efficient multiple images encryption","authors":"Khalid M. Hosny, Sara T. Kamal","doi":"10.1007/s11042-024-20125-x","DOIUrl":"https://doi.org/10.1007/s11042-024-20125-x","url":null,"abstract":"People transmit millions of digital images daily over various networks, where securing these images is a big challenge. Image encryption is a successful approach widely used in securing digital images while transmitting. Researchers developed different encryption techniques that focus on securing individual images. Recently, encryption of multiple images has gained more interest as an emerging encryption approach. In this paper, we proposed a four-tier technique for multiple image encryption (MIE) to increase the transmission speed and improve digital image security. First, we attached the plain images to create an augmented image. Second, the randomized augmented image is obtained by randomly changing the position of each plain image. Third, we scrambled the randomized augmented image using the zigzag pattern, rotation, and random permutation between blocks. Finally, we diffuse the scrambled augmented image using an Altered Sine-logistic-based Tent map (ASLT). We draw a flowchart, write a pseudo-code, and present an illustrative example to simplify the proposed method and make it easy to understand. Many experiments were performed to evaluate this Four-Tier technique, and the results show that this technique is extremely effective and secure to withstand various attacks.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":"35 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optical image encryption based on 3D double-phase encoding algorithm in the gyrator transform domain 基于回旋变换域三维双相编码算法的光学图像加密技术

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-13 DOI: 10.1007/s11042-024-20176-0

Jun Lang, Fan Zhang

{"title":"Optical image encryption based on 3D double-phase encoding algorithm in the gyrator transform domain","authors":"Jun Lang, Fan Zhang","doi":"10.1007/s11042-024-20176-0","DOIUrl":"https://doi.org/10.1007/s11042-024-20176-0","url":null,"abstract":"In this paper, we propose an optical image encryption scheme based on modified 3D double-phase encoding algorithm (3D-DPEA) in the gyrator transform (GT) domain, in which a plaintext is encrypted into two sparse volumetric ciphertexts under the constraints of chaos-generated binary amplitude masks (BAMs). Then, the two volumetric ciphertexts are multiplexed into the corresponding 2D ciphertexts for convenient storage and transmission. First, due to the synergistic adjustment of the two sparse volumetric ciphertexts during the iterative process, the 3D-DPEA would achieve higher recovery quality of the decrypted image with fewer iterations. In addition, because the BAMs are generated by the logistic-tent (LT) chaotic map which is closely related to the rotation angles of GT, and the LT chaotic map has several advantages such as nonlinear, pseudorandom behavior, and high sensitivity of initial conditions, the sensitivity of the secret key could be significantly improved by several orders of magnitude, reaching up to 10−14. As a result, the 3D-DPEA scheme not only eliminates the explicit/linear relationship between the plaintext and the ciphertext but also substantially enhances security. For decryption, the corresponding decrypted image can be achieved by recording an intensity pattern when a coherent beam crosses two sparse volumetric ciphertexts sequentially. Furthermore, BAMs wouldn’t impose an additional burden on the storage and transmission of secret keys. A series of numerical simulations are performed to verify the effectiveness and security of the proposed encryption scheme.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":"121 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0