Background: In the wake of escalating cyber threats and the indispensability of ro-bust network security mechanisms, it becomes crucial to understand the evolving landscape of cryptographic research. Recognizing the s...
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Blockchain is one of the emerging technologies that are applied in various fields and its application in Healthcare 4.0 is crucial to handle the vast amount of health records that are growing continuously everyday. Th...
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Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and *** security systems,such as Intrusion Detection Systems(IDS),are essential ...
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Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and *** security systems,such as Intrusion Detection Systems(IDS),are essential due to the limitations of simpler security measures,such as cryptography and *** to their compact nature and low energy reserves,wireless networks present a significant challenge for security *** features of small cells can cause threats to the *** Coding(NC)enabled small cells are vulnerable to various types of *** attacks and performing secure“peer”to“peer”data transmission is a challenging task in small *** to the low power and memory requirements of the proposed model,it is well suited to use with constrained small *** attacker cannot change the contents of data and generate a new Hashed Homomorphic Message Authentication Code(HHMAC)hash between transmissions since the HMAC function is generated using the shared *** this research,a chaotic sequence mapping based low overhead 1D Improved Logistic Map is used to secure“peer”to“peer”data transmission model using lightweight H-MAC(1D-LM-P2P-LHHMAC)is proposed with accurate intrusion *** proposed model is evaluated with the traditional models by considering various evaluation metrics like Vector Set Generation Accuracy Levels,Key Pair Generation Time Levels,Chaotic Map Accuracy Levels,Intrusion Detection Accuracy Levels,and the results represent that the proposed model performance in chaotic map accuracy level is 98%and intrusion detection is 98.2%.The proposed model is compared with the traditional models and the results represent that the proposed model secure data transmission levels are high.
With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing ac...
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With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec
Medical Internet of Things(IoT)devices are becoming more and more common in *** has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal dat...
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Medical Internet of Things(IoT)devices are becoming more and more common in *** has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized *** methods,while useful,have limitations in predictive accuracy,delay,personalization,and user interpretability,requiring a more comprehensive and efficient approach to harness modern medical IoT *** is a multimodal approach integrating pre-emptive analysis,personalized feature selection,and explainable AI for real-time health monitoring and disease *** using AI for early disease detection,personalized health recommendations,and transparency,healthcare will be *** Multimodal Approach Integrating Pre-emptive Analysis,Personalized Feature Selection,and Explainable AI(MAIPFE)framework,which combines Firefly Optimizer,Recurrent Neural Network(RNN),Fuzzy C Means(FCM),and Explainable AI,improves disease detection precision over existing *** metrics show the model’s superiority in real-time health *** proposed framework outperformed existing models by 8.3%in disease detection classification precision,8.5%in accuracy,5.5%in recall,2.9%in specificity,4.5%in AUC(Area Under the Curve),and 4.9%in delay *** prediction precision increased by 4.5%,accuracy by 3.9%,recall by 2.5%,specificity by 3.5%,AUC by 1.9%,and delay levels decreased by 9.4%.MAIPFE can revolutionize healthcare with preemptive analysis,personalized health insights,and actionable *** research shows that this innovative approach improves patient outcomes and healthcare efficiency in the real world.
As an effective method for data dimensionality reduction, feature selection could improve the classification accuracy and reduce the computational cost when dealing with high-dimensional data. Feature selection is ess...
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Open-vocabulary object detection (OVD) models are considered to be Large Multi-modal Models (LMM), due to their extensive training data and a large number of parameters. Mainstream OVD models prioritize object coarse-...
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Vehicular Ad-Hoc Networks (VANETs) are studied wireless networks that enable communication among vehicles and roadside infrastructure. The role a vital play in improving on-road safety, efficacy, and convenience by en...
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The existing cloud model unable to handle abundant amount of Internet of Things (IoT) services placed by the end users due to its far distant location from end user and centralized nature. The edge and fog computing a...
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In the realm of video understanding tasks, Video Transformer models (VidT) have recently exhibited impressive accuracy improvements in numerous edge devices. However, their deployment poses significant computational c...
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In the realm of video understanding tasks, Video Transformer models (VidT) have recently exhibited impressive accuracy improvements in numerous edge devices. However, their deployment poses significant computational challenges for hardware. To address this, pruning has emerged as a promising approach to reduce computation and memory requirements by eliminating unimportant elements from the attention matrix. Unfortunately, existing pruning algorithms face a limitation in that they only optimize one of the two key modules on VidT's critical path: linear projection or self-attention. Regrettably, due to the variation in battery power in edge devices, the video resolution they generate will also change, which causes both linear projection and self-attention stages to potentially become bottlenecks, the existing approaches lack generality. Accordingly, we establish a Run-Through Sparse Attention (RTSA) framework that simultaneously sparsifies and accelerates two stages. On the algorithm side, unlike current methodologies conducting sparse linear projection by exploring redundancy within each frame, we extract extra redundancy naturally existing between frames. Moreover, for sparse self-attention, as existing pruning algorithms often provide either too coarse-grained or fine-grained sparsity patterns, these algorithms face limitations in simultaneously achieving high sparsity, low accuracy loss, and high speedup, resulting in either compromised accuracy or reduced efficiency. Thus, we prune the attention matrix at a medium granularity—sub-vector. The sub-vectors are generated by isolating each column of the attention matrix. On the hardware side, we observe that the use of distinct computational units for sparse linear projection and self-attention results in pipeline imbalances because of the bottleneck transformation between the two stages. To effectively eliminate pipeline stall, we design a RTSA architecture that supports sequential execution of both sparse linear pro
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