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QSroute : A QoS Aware Routing Scheme for Software Defined networking

QSroute : A QoS Aware Routing Scheme for Software Defined Ne...

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International conference on Software Engineering and Computer Systems (ICSECS)

作者： Imran Edzereiq Kamarudin Mohamed Ariff Ameedeen Muhamad Idaham Umar Ong Azlee Zabidi Department of Computer Systems and Networking Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia Department of Software Engineering Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia

The increasing demand for bandwidth-intensive network applications, such as video streaming, multimedia, and Internet of Things (IoT) applications, necessitates improved resource management to protect the network without compromising Quality of Service (QoS). Meeting these challenges requires a centralized view of all available network resources. Software Defined networking (SDN), an emerging technology, provides a centralized view and control of network resources. This feature enables administrators to programmatically define and manage network behavior, including routing, making it more flexible and adaptable. This study proposes a QoS-aware routing scheme for SDN that considers available bandwidth, packet delay, and packet loss to determine the optimal routing path. Optimal paths are selected based on meeting predefined threshold criteria. The study concludes by discussing potential directions for future research in this field.

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Collaborative Video Cache Management Strategy in Mobile Edge computing

Collaborative Video Cache Management Strategy in Mobile Edge...

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IEEE Wireless Communications and networking conference (WCNC)

作者： Sang, Zihao Guo, Songtao Wang, Ying Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

ISBN: (纸本)9781728195056

With the rapid development of multimedia services in wireless mobile networks, the video data traffic has increased exponentially. Traditional video traffic service based on cloud computing caused a large amount of traffic load and longer access delay, which severely reduces the quality of service (QoS) of users. Mobile edge caching as one of the use cases of mobile edge computing (MEC), can directly serve user requests so as to greatly reduce the traffic load and shorten access delay. At present, distributed caching is widely used in the caching deployment of base stations (BSs). However, the caching capacity of a single BS is generally particularly limited, which will degrade the performance of wireless mobile network. In this paper, a collaborative caching strategy in the heterogeneous MEC networks is designed, and the edge caching of macro base station (MBS) and small base stations (SBSs) is utilized to bring storage resources closer to users. In addition, an optimization problem of content caching is formulated to minimize the total delay cost of all users requesting content in the MEC networks. In order to solve this problem, cache management strategy (GCS) is proposed, which consists of greedy cache placement strategy and greedy cache update strategy. Finally, numerical simulations demonstrate that the GCS scheme effectively improves the cache hit rate, and significantly reduces the average delay and backhaul traffic load.

关键词： Mobile edge computing collaborative caching quality of service cache management strategy

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Implementation of Machine Learning-Based QoS Traffic Classification In Software-Defined networking

Implementation of Machine Learning-Based QoS Traffic Classif...

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Asia-Pacific conference on Applied Electromagnetics, APACE

作者： Nur Fatin Nabilah Mohd Fauzi Tan Saw Chin Fatimah Audah Md Zaki Faculty of Computing & Informatics Multimedia University Malaysia

ISBN: (数字)9798350364163

ISBN: (纸本)9798350364170

The concept of Software-Defined networking (SDN) in network management involves controlling network behaviour through software applications. The implementation of SDN centralizes network management, ensuring efficient performance and reducing computational costs. With the vast amount of network traffic today, traffic classification is crucial in SDN, improving performance by prioritizing applications that require higher Quality of Service (QoS). The occurrence of imbalanced datasets, which focus more on the majority class rather than the minority class, leads to poor performance in SDN. This paper explores the implementation of feature selection using filter and wrapper methods to tackle imbalanced datasets and enhance network management performance. Using a real-time dataset containing 930 features from the University of Cambridge, the Symmetrical Uncertainty and C4.5 algorithm achieve a classification accuracy of 97.69% and reduced the dataset size by 50%. These findings demonstrate that the proposed methods significantly improve computational efficiency and classification performance, making them suitable for real-time SDN applications.

关键词： Accuracy Uncertainty Machine learning algorithms Quality of service Telecommunication traffic Filtering algorithms Real-time systems Software Computational efficiency Software defined networking

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LLM-Enhanced Deepfake Detection: Dense CNN and Multi-Modal Fusion Framework for Precise multimedia Authentication

LLM-Enhanced Deepfake Detection: Dense CNN and Multi-Modal F...

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Advances in Data Engineering and Intelligent computing Systems (ADICS), International conference on

作者： Samesh Enathe VP Chandra Mouli S R. Dheepthi Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The increasing ubiquity of deepfake technology presents a serious risk to the legitimacy and reliability of multimedia material across a number of sectors, from identity verification to news distribution. Current deepfake detection methods frequently have trouble identifying minor manipulations and are unable to keep up with the latest generation techniques, which creates a serious vulnerability in the defence against the improper use of synthetic media. Using Dense Convolutional Neural Networks (Dense CNN) and Multi-Modal Fusion, this study presents a novel method for deepfake detection. Our Dense CNN architecture which draws inspiration from Dense Net improves sensitivity to complex manipulations while optimizing feature reuse through dense connectivity patterns, thereby mitigating the shortcomings of existing systems. Our suggested approach dynamically combines temporal and visual modalities, enhanced by Multi-Modal Fusion, to offer a comprehensive contextual knowledge that enhances detection accuracy. After thorough tests on several datasets, our method performs exceptionally well and is particularly good at identifying advanced deepfake variations. Our suggested methodology improves the state-of-the-art in deepfake identification by addressing the shortcomings of current systems and providing a reliable response to the urgent problems brought on by the malicious usage of synthetic media in practical applications. Adding Large Language Models (LLMs) is essential to improving the accuracy of the system. To add another level of scrutiny, LLMs are deliberately used to characterize and analyze portions of multimedia information that are vulnerable to manipulation.

关键词： Deepfakes Visualization Sensitivity Social networking (online) Materials reliability Media Data engineering

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Area Intervention for Enhancing Class Activation Maps in Weakly Supervised Semantic Segmentation

Area Intervention for Enhancing Class Activation Maps in Wea...

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IEEE International conference on multimedia and Expo (ICME)

作者： Xuewei Li Yujie Diao Mei Yu Chenhan Wang Jie Gao Ruiguo Yu College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China School of Feature Technology Tianjin University Tianjin China OpenBayes (Tianjin) IT Co. Ltd. Tianjin China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Generating class activation maps (CAM) as seed regions is a crucial step in weakly supervised semantic segmentation (WSSS). During the training of classification models, instance area information is incorporated into feature vectors and learned by the network. However, during CAM generation, pixel-level prediction scores are solely dependent on features and classifier weights, neglecting the area factor. Such area information becomes a source of bias between training and CAM generation, leading to a significant under-activation (false negatives) of foreground regions. To address this issue, we propose a class area intervention strategy to fine-tune the classification model and rectify this bias. Additionally, we employ a partition activation strategy that can simultaneously consider both under activation and over-activation to generate more precise AICAMs. AICAM is applicable to various WSSS methods based on CAM or its variants. The evaluations on both the PASCAL VOC and MS COCO datasets demonstrate that our method enhances multiple WSSS techniques.

关键词： Training Semantic segmentation Vectors

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Duplicate Account Detection on Instagram, Twitter, and YouTube Using Deep Learning

Duplicate Account Detection on Instagram, Twitter, and YouTu...

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Artificial Intelligence, Blockchain, Cloud computing, and Data Analytics (ICoABCD), International conference on

作者： Muhammad Hanif Firmansyah Niken Dwi Wahyu Cahyani Satria Akbar Mugitama School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798350368451

ISBN: (纸本)9798350368468

In the digital era, social media platforms like Instagram, Twitter, and YouTube are pivotal for communication, business, politics, and education. However, the proliferation of duplicate accounts used for manipulative or harmful purposes presents significant challenges to online integrity and security. Current detection methods, which often rely on manual analysis or simple heuristics, are inadequate for the growing complexity and volume of data. This paper proposes an adaptive method for detecting duplicate accounts on these platforms, leveraging machine learning and big data analytics. By utilizing Deep Learning Recurrent Neural Networks (RNN) for textual data, the proposed approach aims to improve the accuracy and efficiency of duplicate account detection. The results of this study outline step to detect duplicate accounts using criteria gathered from previous research and also simplify the process of identifying which accounts are likely to be duplicates.

关键词： Deep learning Video on demand Recurrent neural networks Social networking (online) Blogs Time series analysis Web sites multimedia communication Security Reliability

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Safeguard Feature: Instagram Followers' Personal Information

Safeguard Feature: Instagram Followers' Personal Information

引用

Advances in computing, Communication and networking (ICAC2N), International conference on

作者： Sukrit Kumar Bal Krishna Saraswat Sumit Kumar Madhavi Tripathi Avantika Sharma Computer Science and Engineering Department Sharda University Greater Noida Uttar Pradesh India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

In a digitalized market, social connections are on the top as an essential part of daily life, enabling users to connect, share, and engage with content on a global scale. Among these platforms, Instagram stands out as a prominent player, boasting over a billion active users worldwide. However, with the widespread use of Instagram comes significant concerns regarding the safeguarding of followers' personal information. This research paper examines the various strategies and mechanisms employed by Instagram to protect user data and privacy. It explores Instagram's security features, including encryption, authentication mechanisms, and account privacy settings, which are designed to mitigate risks such as unauthorized access, data breaches, and exploitation of personal information. Additionally, the paper discusses the importance of user education and awareness in promoting responsible data stewardship and mitigating privacy risks on the platform. By analyzing Instagram's efforts to safeguard follower's personal information, this paper aims to provide insights into the complexities and challenges inherent in today's digital landscape and contribute to the ongoing dialogue surrounding online privacy and data protection.

关键词： Privacy Education Data protection Data breach Control systems User experience Encryption Web sites multimedia communication Security

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Energy-Efficient Anti-Jamming Metaverse Resource Allocation Based on Reinforcement Learning

Energy-Efficient Anti-Jamming Metaverse Resource Allocation ...

引用

IEEE conference on Wireless Communications and networking

作者： Yiwen Zhan Jinming Zhang Zhiping Lin Liang Xiao Dept. of Information and Communication Engineering Xiamen University Xiamen China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Metaverse system facilitated by Extended Reality (XR) requires extensive computing and communication resources to provide seamless services for users and has to resist jamming attacks. In this paper, we propose a reinforcement learning based Metaverse resource allocation scheme against jamming, which optimizes the rendering mode, transmit channel, and power to fulfill the quality of experience (QoE) requirements for Metaverse services. This scheme incorporates both the background resolution, determined by the linear model of visual acuity decline, and the background correlation into the state formulation and rendering process, with both factors influencing the data size of the rendering task. Based on the required resolution, the background resolution, the data size of foreground and background, the background correlation, and the radio channel gain, the 2-level hierarchical architecture evaluates the expected utility of the rendering policy in the first level, and the transmission policy in the second level, and estimates the risk value depending on the latency to mitigate the risk of user experience disruption. Simulation results show that our proposed scheme reduces the service latency, conserves energy consumption, and enhances the QoE of Metaverse users.

关键词： Visualization Correlation Metaverse Reinforcement learning Rendering (computer graphics) Energy efficiency Resource management

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Image Compression Algorithms - Comparative Analysis

Image Compression Algorithms - Comparative Analysis

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International conference on

作者： G. Niharika Reddy M. Namrutha Namratha Akshaya Sreebha Bhaskaran Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Due to the abundance of the new digital media data, the issue of image quality and volume of data requiring compression has become a significant factor of concern, especially in media storage and transmitting. This work affords a comparative analysis of different image compression techniques with focus on the compression ratio, quality preservation, and complexity. A new hybrid model of Predictive Coding, Run Length Coding and Quantum Entropy Coding (QEC) is proposed and shown to exhibit negligible quality loss with substantial space saving. The experimental outcomes show that the proposed method reduces space 80 percent and works better than previous methods for areas requiring high speed and relative accuracy. These insights are timely, as practical computing-communication trade-offs are paramount in the new generation of social networks, medicine, and multimedia streaming.

关键词： Integrated circuits Image quality Image coding Social networking (online) Streaming media Predictive coding Media Predictive models Prediction algorithms Entropy coding

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XGBoost Classification of XAI based LIME and SHAP for Detecting Dementia in Young Adults

XGBoost Classification of XAI based LIME and SHAP for Detect...

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International conference on computing and networking Technology (ICCNT)

作者： Vandana Sharma Divya Midhunchakkaravarthy Department of Computer Science and Multimedia Lincoln University College Kuala Lumpur Malaysia CHRIST (Deemed to be University) Delhi-NCR India

As technology progresses on a fast pace, it is imperative that shall be used in the field of medicine for the early detection and diagnostics of dementia. Dementia affects humans by deteriorating the cognitive functions, and as such many algorithms have been used in the detection of the same but all these algorithms remain a black box to the medical fraternity which is still dubious about the nature and credibility of the prediction. To ease this issue, the use of explainable artificial intelligence has been proposed and implemented in this paper, which makes it easy to understand why and how the model is giving a particular output. In this paper the XGBoost classification algorithm has been used which give an accuracy of 93.33% and to understand these predictions, two separate algorithms namely Local Interpretable Model-agnostic Explanations (LIME) and Shapely Additive Explanations (SHAP) have been used. These algorithms are compared based on the type of explanation they provide for the same input and thus the weakness of LIME algorithm has been found out at certain intervals based on the clinically important features of the dataset. On the other hand, both the algorithms make it easy for medical practitioners to understand the dominating factors of a predicted output thereby helping to eliminate the black-box nature of dementia detection.

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