Significant research efforts are currently devoted to wireless sensor networks due to its broad range of applications. WSNs face various constraints, encompassing challenges related to communication, clustering manage...
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Significant research efforts are currently devoted to wireless sensor networks due to its broad range of applications. WSNs face various constraints, encompassing challenges related to communication, clustering management and the finite battery life of nodes. Thus, Energy conservation in such networks is indispensable. Given a constant energy consumption rate during information sensing and reception, the highest energy consumption among sensor nodes occurs during data transmission. One of promising solution to reduce energy consumption is organizing WSN in clusters. Clustering in Wireless Sensor Networks (WSN) involves grouping sensor nodes into clusters to facilitate efficient data aggregation, communication, and management within the network. This organizational structure helps optimize energy consumption, enhance scalability, and prolong the overall lifespan of the WSN. However determining the optimal criteria for selecting cluster heads is challenging, as it involves balancing energy efficiency, network connectivity, and load distribution. In this paper, a dual-phase approach is proposed, firstly Reinforcement learning (RL) approach has been applied to clustering in WSNs which enables nodes to autonomously adapt their clustering strategies, leading to more efficient and adaptive network configurations. Further Particle Swarm Optimization (PSO) can be utilized for cluster head selection in Wireless Sensor Networks (WSNs) to optimize the formation of clusters. The consideration of both local and global perspectives in the proposed approach results in a more balanced and efficient clustering solution. The outcomes of our experiments demonstrate the enhanced performance of the integrated approach as compared to traditional clustering algorithms. Results show considerable improvement in terms of reduced energy consumption, accuracy and efficiency in fault detection specifically tailored for Wireless Sensor Networks (WSNs). In addition the proposed algorithm show enha
The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle,profoundly impeding their effective utilization across various *** methodologies have emerged as pivotal components...
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The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle,profoundly impeding their effective utilization across various *** methodologies have emerged as pivotal components of image preprocessing,fostering an improvement in the quality of remote sensing *** enhancement renders remote sensing data more indispensable,thereby enhancing the accuracy of target *** defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed *** response to this challenge,a novel UNet Residual Attention Network(URA-Net)is *** paradigmatic approach materializes as an end-to-end convolutional neural network distinguished by its utilization of multi-scale dense feature fusion clusters and gated jump *** essence of our methodology lies in local feature fusion within dense residual clusters,enabling the extraction of pertinent features from both preceding and current local data,depending on contextual *** intelligently orchestrated gated structures facilitate the propagation of these features to the decoder,resulting in superior outcomes in haze *** validation through a plethora of experiments substantiates the efficacy of URA-Net,demonstrating its superior performance compared to existing methods when applied to established datasets for remote sensing image *** the RICE-1 dataset,URA-Net achieves a Peak Signal-to-Noise Ratio(PSNR)of 29.07 dB,surpassing the Dark Channel Prior(DCP)by 11.17 dB,the All-in-One Network for Dehazing(AOD)by 7.82 dB,the Optimal Transmission Map and Adaptive Atmospheric Light For Dehazing(OTM-AAL)by 5.37 dB,the Unsupervised Single Image Dehazing(USID)by 8.0 dB,and the Superpixel-based Remote Sensing Image Dehazing(SRD)by 8.5 *** noteworthy,on the SateHaze1k dataset,URA-Net attains preeminence in overall performance,yieldi
In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)a...
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In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.
This article investigates the impact of Artificial Intelligence (AI) and ChatGPT in the business sector. It highlights the evolution of AI, focusing on the integration and applications of technologies like machine lea...
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Social media platforms like Twitter and Facebook have gradually become vital for communication and information exchange. However, this often leads to the spread of unreliable or false information, such as harmful rumo...
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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 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
Forest fires, a dangerous natural phenomenon, cause large-scale destruction in forests and nearby communities. In this paper, we leverage the capabilities of classification and fast prediction of machine learning and ...
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Healthcare is a fundamental part of every individual’s *** healthcare industry is developing very rapidly with the help of advanced *** researchers are trying to build cloud-based healthcare applications that can be ...
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Healthcare is a fundamental part of every individual’s *** healthcare industry is developing very rapidly with the help of advanced *** researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises,as well as by patients from their mobile devices through communication *** systems promote reliable and remote interactions between patients and healthcare ***,there are several limitations to these innovative cloud computing-based systems,namely network availability,latency,battery life and resource *** propose a hybrid mobile cloud computing(HMCC)architecture to address these ***,we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed *** compare them,to identify the strengths and weaknesses of each algorithm;and provide their comparative results,to show latency and energy consumption *** issues for cloudbased healthcare systems are discussed in detail.
Thyroid disorders are increasingly prevalent, making early detection crucial for reducing mortality and complications. Accurate prediction of disease progression and understanding the interplay of clinical features ar...
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Exploration strategy design is a challenging problem in reinforcement learning(RL),especially when the environment contains a large state space or sparse *** exploration,the agent tries to discover unexplored(novel)ar...
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Exploration strategy design is a challenging problem in reinforcement learning(RL),especially when the environment contains a large state space or sparse *** exploration,the agent tries to discover unexplored(novel)areas or high reward(quality)*** existing methods perform exploration by only utilizing the novelty of *** novelty and quality in the neighboring area of the current state have not been well utilized to simultaneously guide the agent’s *** address this problem,this paper proposes a novel RL framework,called clustered reinforcement learning(CRL),for efficient exploration in *** adopts clustering to divide the collected states into several clusters,based on which a bonus reward reflecting both novelty and quality in the neighboring area(cluster)of the current state is given to the *** leverages these bonus rewards to guide the agent to perform efficient ***,CRL can be combined with existing exploration strategies to improve their performance,as the bonus rewards employed by these existing exploration strategies solely capture the novelty of *** on four continuous control tasks and six hard-exploration Atari-2600 games show that our method can outperform other state-of-the-art methods to achieve the best performance.
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