The increasing prevalence of child trafficking in the South particularly in Bangladesh, India, and Nepal requires a creative solution that can go beyond the orthodox preventative techniques. This continuously growing ...
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For Future networks, many research projects have proposed different architectures around the globe;Software Defined Network(SDN) architectures, through separating Data and Control Layers, offer a crucial structure for...
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For Future networks, many research projects have proposed different architectures around the globe;Software Defined Network(SDN) architectures, through separating Data and Control Layers, offer a crucial structure for it. With a worldwide view and centralized Control, the SDN network provides flexible and reliable network management that improves network throughput and increases link utilization. In addition, it supports an innovative flow scheduling system to help advance Traffic engineering(TE). For Medium and large-scale networks migrating directly from a legacy network to an SDN Network seems more complicated & even impossible, as there are High potential challenges, including technical, financial, security, shortage of standards, and quality of service degradation challenges. These challenges cause the birth and pave the ground for Hybrid SDN networks, where SDN devices coexist with traditional network devices. This study explores a Hybrid SDN network’s Traffic engineering and Quality of Services Issues. Quality of service is described by network characteristics such as latency, jitter, loss, bandwidth,and network link utilization, using industry standards and mechanisms in a Hybrid SDN Network. We have organized the related studies in a way that the Quality of Service may gain the most benefit from the concept of Hybrid SDN networks using different algorithms and mechanisms: Deep Reinforcement Learning(DRL), Heuristic algorithm, K path partition algorithm, Genetic algorithm, SOTE algorithm, ROAR method, and Routing Optimization with different optimization mechanisms that help to ensure high-quality performance in a Hybrid SDN Network.
Understanding and predicting air quality is pivotal for public health and environmental management, especially in urban areas like Delhi. This study utilizes a comprehensive dataset from the Central Pollution Control ...
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To efficiently mine threat intelligence from the vast array of open-source cybersecurity analysis reports on the web,we have developed the Parallel Deep Forest-based Multi-Label Classification(PDFMLC)***,open-source c...
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To efficiently mine threat intelligence from the vast array of open-source cybersecurity analysis reports on the web,we have developed the Parallel Deep Forest-based Multi-Label Classification(PDFMLC)***,open-source cybersecurity analysis reports are collected and converted into a standardized text ***,five tactics category labels are annotated,creating a multi-label dataset for tactics *** the limitations of low execution efficiency and scalability in the sequential deep forest algorithm,our PDFMLC algorithm employs broadcast variables and the Lempel-Ziv-Welch(LZW)algorithm,significantly enhancing its acceleration ***,our proposed PDFMLC algorithm incorporates label mutual information from the established dataset as input *** captures latent label associations,significantly improving classification ***,we present the PDFMLC-based Threat Intelligence Mining(PDFMLC-TIM)*** results demonstrate that the PDFMLC algorithm exhibits exceptional node scalability and execution ***,the PDFMLC-TIM method proficiently conducts text classification on cybersecurity analysis reports,extracting tactics entities to construct comprehensive threat *** a result,successfully formatted STIX2.1 threat intelligence is established.
In this work, an earthquake prediction system utilizing machine learning (ML) techniques and Internet of Things (iot) technologies is presented, using accelerometer data from the ADXL335 sensor. In order to analyze se...
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ISBN:
(纸本)9798350393354
In this work, an earthquake prediction system utilizing machine learning (ML) techniques and Internet of Things (iot) technologies is presented, using accelerometer data from the ADXL335 sensor. In order to analyze seismic patterns, the system records multi-axis accelerations. Various machine learning models are then used for predictive analytics. This technology seeks to predict probable seismic events by combining sensor data with sophisticated algorithms, assisting early warning systems for disaster readiness. The ADXL335 accelerometer is the central component of the Earthquake Prediction System described in this work. It records accelerations on the X, Y, and Z axes and converts them into analogue signals for further processing. These data streams are transmitted for feature extraction by utilizing iot infrastructure, with an emphasis on seismic patterns that may indicate future earthquake events. To evaluate the accelerometer data and produce predicted insights, the system incorporates a variety of machine learning models, such as decision trees and support vector machines. The goal is to support disaster management plans by enabling early detection and warning of seismic activity through this combination of sensor technology and advanced analytics. A wide variety of machine learning models, such as decision trees, support vector machines, and recurrent neural networks, are used to derive actionable insights. These algorithms produce predictive analytics to support catastrophe management methods by carefully analyzing accelerometer data. The ultimate objective is to enable more proactive disaster mitigation planning by facilitating early detection and alerts of seismic activity. This system, which combines advanced analytics with sensor technology, is a critical step in strengthening disaster management systems. Its capacity to predict seismic events may help minimize the effects of earthquakes on impacted areas, help with evacuation plans, and provide timely a
The coronavirus disease 2019(COVID-19)and its mutant viruses are still wreaking global havoc over the last two years,but the impact of human activity on the transmission of the pandemic is difficult to *** human dynam...
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The coronavirus disease 2019(COVID-19)and its mutant viruses are still wreaking global havoc over the last two years,but the impact of human activity on the transmission of the pandemic is difficult to *** human dynamic spatiotemporal distribution can help in our understanding of how to mitigate COVID-19 spread,which can help in maintaining urban health within a county and between counties within a *** distribution can be computed using the Volunteered Geographic Information(VGI)of the citizens in conjunction with other variables,such as climatic conditions,and used to analyze how human’s daily density distribution quantitatively affects COVID-19 *** on the estimated population density,when the population density increases daily by 1 person/km^(2) in a county or prefectural-level administrative unit with an average size of 26,000 km^(2),the county would have an additional 3.6 confirmed cases and 0.054 death cases after 5 days,which is the illness onset time for a new COVID-19 *** 14 days,which is the maximum incubation period of the COVID-19 virus,there would be 5 new confirmed cases and 0.092 death ***,in neighboring regions,there can be 0.96 fewer people infected with COVID-19 on average per day as a result of strong intervention of local and neighboring *** primary innovation and contribution are that this is the first quantitative assessment of the impacts of dynamic population density on the COVID-19 ***,the direct and indirect effects of the impact are estimated using spatial panel *** models that control the unobserved factors improve the reliability of the estimation,as validated by random experiments and the use of the Baidu migration dataset.
Urban traffic congestion poses a major challenge, particularly for emergency services like ambulances, where delays can have life-threatening consequences. This paper proposes a novel, cost-efficient Automated Traffic...
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In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network *** research in...
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In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network *** research introduces a sophisticated framework,driven by computational intelligence,that merges clustering techniques with UAV mobility to refine routing strategies in *** proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads(CHs).This system is primarily aimed at reducing energy consumption through meticulously planned routing and path *** a greedy algorithm for inter-cluster dialogue,our framework orchestrates CHs into an efficient communication *** comparative analysis,the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing(CCMAR)and the energy-efficient cluster-based dynamic algorithms(ECCRA).Specifically,it showcases an impressive 15%increase in energy conservation and a 20%reduction in data transmission time,highlighting its advanced ***,this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN,emphasizing the vital role of sophisticated computational strategies in optimizing network operations.
For dynamic application scenarios of Mobile Edge Computing(MEC),an Energy-efficient Multiuser and Multitask Computation Offloading(EMMCO)optimization method is *** the consideration of multiuser and multitask computat...
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For dynamic application scenarios of Mobile Edge Computing(MEC),an Energy-efficient Multiuser and Multitask Computation Offloading(EMMCO)optimization method is *** the consideration of multiuser and multitask computation offloading,first,the EMMCO method takes into account the existence of dependencies among different tasks within an implementation,abstracts these dependencies as a Directed Acyclic Graph(DAG),and models the computation offloading problem as a Markov decision ***,the task embedding sequence in the DAG is fed to the RNN encoder-decoder neural network with combination of the attention mechanism,the long-term dependencies among different tasks are successfully captured by this ***,the Improved Policy Loss Clip-based PPO2(IPLC-PPO2)algorithm is developed,and the RNN encoder-decoder neural network is trained by the developed *** loss function in the IPLC-PPO2 algorithm is utilized as a preference for the training process,and the neural network parameters are continuously updated to select the optimal offloading scheduling *** results demonstrate that the proposed EMMCO method can achieve lower latency,reduce energy consumption,and obtain a significant improvement in the Quality of Service(QoS)than the compared algorithms under different situations of mobile edge network.
With the increasing pervasiveness of mobile devices such as smartphones,smart TVs,and wearables,smart sensing,transforming the physical world into digital information based on various sensing medias,has drawn research...
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With the increasing pervasiveness of mobile devices such as smartphones,smart TVs,and wearables,smart sensing,transforming the physical world into digital information based on various sensing medias,has drawn researchers’great *** different sensing medias,WiFi and acoustic signals stand out due to their ubiquity and zero hardware *** on different basic principles,researchers have proposed different technologies for sensing applications with WiFi and acoustic signals covering human activity recognition,motion tracking,indoor localization,health monitoring,and the *** enable readers to get a comprehensive understanding of ubiquitous wireless sensing,we conduct a survey of existing work to introduce their underlying principles,proposed technologies,and practical *** we also discuss some open issues of this research *** survey reals that as a promising research direction,WiFi and acoustic sensing technologies can bring about fancy applications,but still have limitations in hardware restriction,robustness,and applicability.
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