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Showing papers in "Multimedia Tools and Applications in 2023"





Journal ArticleDOI
TL;DR: In this paper , a fuzzy delay-bandwidth guaranteed routing (FDBGR) algorithm is proposed that considers both delay and bandwidth constraints in routing, where the purpose of FDBGR is to distribute the network workload evenly for all requests, where this is done by maintaining the capacity to accept future requests.
Abstract: Video conferencing is one of the advanced technologies for users that allows online communication despite long distances. High quality communication and ongoing support for the principles of video conferencing service that can be achieved through Software-Defined Networking (SDN). SDN is a new architecture for computer networks that separates the control plane from the data plane to improve network resources and reduce operating costs. All routing decisions and control mechanisms are made by a device called a controller. Traffic engineering can be well implemented in SDN because the entire network topology is known to the controller. Considering SDN features, user requests can be dynamically routed according to current network status and Quality of Service (QoS) requirements. In general, the purpose of SDN routing algorithms is to maximize the acceptance rate of user requests by considering QoS requirements. In this literature, most routing studies to provide satisfactory video conferencing services have focused solely on bandwidth. Nevertheless, some studies have considered both delay and bandwidth constraints. In this paper, a Fuzzy Delay-Bandwidth Guaranteed Routing (FDBGR) algorithm is proposed that considers both delay and bandwidth constraints in routing. The proposed fuzzy system is based on rules that can postpone requests with high resource demands. Also, the purpose of the FDBGR is to distribute the network workload evenly for all requests, where this is done by maintaining the capacity to accept future requests. The combination of conventional routing algorithms and SDN provides remarkable improvements in mobility, scalability and the overall performance of the networks. Simulations are performed on different scenarios to evaluate the performance of the FDBGR compared to state-of-the-art methods. Besides, FDBGR has been compared with a number of most related previous works such as H-MCOP, MH-MCOP, QoMRA, QROUTE and REDO based on criteria such as number of accepted requests, average path length, energy consumption, load balancing, and average delay. The simulation results clearly prove the superiority of the proposed algorithm with an average delay of 48 ms in different topologies for video conferencing applications.

7 citations



Journal ArticleDOI
TL;DR: This work proposes an image encryption approach that addresses constraints in WBAN by utilizing adaptive DNA code bases and a new multi chaotic map architecture and results indicated that this scheme has a strong level of security.

6 citations







Journal ArticleDOI
TL;DR: In this article , the authors proposed a transfer learning based feature fusion process (FFP) which enables the removal of hyper-parameter tuning, improves model performance, and lowers the computational burden of the desired model.
Abstract: Deep learning-based methods have been proven excellent performance in detecting pornographic images/videos flooded on social media. However, in a dearth of huge yet well-labeled datasets, these methods may suffer from under/overfitting problems and may exhibit unstable output responses in the classification process. To deal with the issue we have suggested an automatic pornographic image detection method by utilizing transfer learning (TL) and feature fusion. The novelty of our proposed work is TL based feature fusion process (FFP) which enables the removal of hyper-parameter tuning, improves model performance, and lowers the computational burden of the desired model. FFP fuses low-level and mid-level features of the outperforming pre-trained models followed by transferring the learned knowledge to control the classification process. Key contributions of our proposed method are i) generation of a well-labeled obscene image dataset GGOI via Pix-2-Pix GAN architecture for the training of deep learning models ii) modification of model architectures by integrating batch normalization and mixed pooling strategy to obtain training stability (iii) selection of outperforming models to be integrated with the FFP by performing end-to-end detection of obscene images and iv) design of TL based obscene image detection method by retraining the last layer of the fused model. Extensive experimental analyses are performed on benchmark datasets i.e., NPDI, Pornography 2k, and generated GGOI dataset. The proposed TL model with fused MobileNet V2 + DenseNet169 network performs as the state-of-the-art model compared to existing methods and provides average classification accuracy, sensitivity, and F1 score of 98.50%, 98.46% and 98.49% respectively.









Journal ArticleDOI
TL;DR: In this article , a novel approach for integrated IoT (Internet of Things) with Blockchain in Health Supply Chain (NAIBHSC) approach is introduced. But, the approach is not suitable for the use of IoT devices in the real world.
Abstract: Nowadays blockchain technology plays a vital role in creative developments and important discoveries in the world. Blockchain develops secure and trustworthy platforms for data sharing in various application areas such as secure sharing of medical data, Anti-money laundering, tracking systems, Supply chain, and logistics monitoring, Crypto-currency exchange, etc. Today's Supply chain in the healthcare sector faces many problems like security, transparency, tampering with medical products, counterfeit drugs, more paperwork, high cost, and more time-consuming process while transporting medical equipment from manufacture to end-users. To overcome these problems, we introduce Novel Approach for Integrated IoT (Internet of Things) With Blockchain in Health Supply Chain (NAIBHSC) approach. By using this approach, we can eliminate all supply chain-related issues between suppliers and end-users. The goal of this research is by combining Blockchain technology with IoT to develop a smart health supply chain management system. This approach provides security, privacy, trust, visibility, decentralized tracking and tracing of the medical product, avoids counterfeit drugs, avoids the damage to medical components, authentication, reduces the cost, and provides the status of the products during the shipment process between manufacturers to end-user. In this approach, we conduct a series of experiments on a different group of users. The experimental results show that compare to existing approaches our proposed NAIBHSC approach gives better response time that is the average Transaction Per Second (TPS) for a group of 500 users is 100 milliseconds, reduces the latency time that is average latency time for 500 users group has 403 milliseconds, and improves the overall performance of the smart health supply chain management system.


Journal ArticleDOI
TL;DR: This paper proposes a novel method to detect the forged faces using Image Quality Assessment(IQA) based features and has achieved the highest accuracy of 99% when different types of experiments were performed on standard datasets.


Journal ArticleDOI
TL;DR: In this paper , a discrete wavelet optimized network model was proposed for COVID-19 diagnosis and feature extraction, which consists of three stages pre-processing, feature extraction and classification, and achieved the performance of 99, 100, 98.5%, and 99.5% for the CT dataset, and the accomplished accuracy, specificity, sensitivity, and precision values of the proposed model for the X-ray dataset are 98, 99, 98% and 97% respectively.
Abstract: Recently, the Covid-19 pandemic has affected several lives of people globally, and there is a need for a massive number of screening tests to diagnose the existence of coronavirus. For the medical specialist, detecting COVID-19 cases is a difficult task. There is a need for fast, cheap and accurate diagnostic tools. The chest X-ray and the computerized tomography (CT) play a significant role in the COVID-19 diagnosis. The advancement of deep learning (DL) approaches helps to introduce a COVID diagnosis system to achieve maximum detection rate with minimum time complexity. This research proposed a discrete wavelet optimized network model for COVID-19 diagnosis and feature extraction to overcome these problems. It consists of three stages pre-processing, feature extraction and classification. The raw images are filtered in the pre-processing phase to eliminate unnecessary noises and improve the image quality using the MMG hybrid filtering technique. The next phase is feature extraction, in this stage, the features are extracted, and the dimensionality of the features is diminished with the aid of a modified discrete wavelet based Mobile Net model. The third stage is the classification here, the convolutional Aquila COVID detection network model is developed to classify normal and COVID-19 positive cases from the collected images of the COVID-CT and chest X-ray dataset. Finally, the performance of the proposed model is compared with some of the existing models in terms of accuracy, specificity, sensitivity, precision, f-score, negative predictive value (NPV) and positive predictive value (PPV), respectively. The proposed model achieves the performance of 99%, 100%, 98.5%, and 99.5% for the CT dataset, and the accomplished accuracy, specificity, sensitivity, and precision values of the proposed model for the X-ray dataset are 98%, 99%, 98% and 97% respectively. In addition, the statistical and cross validation analysis is conducted to validate the effectiveness of the proposed model.