Crop assessment plays an important role in ensuring food safety, and recent technological advances such as machine learning and deep learning have revolutionized assessment, and crop and culture management. Agriconnec...
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With the development of the sixth-generation network, Digital Twin (DT) is driving the explosive growth of Internet-of-Vehicles (IoVs). The rapid proliferation of highly mobile IoVs, coupled with advanced applications...
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With the development of the sixth-generation network, Digital Twin (DT) is driving the explosive growth of Internet-of-Vehicles (IoVs). The rapid proliferation of highly mobile IoVs, coupled with advanced applications, resulted in rigorous demands for quality of experience (QoE) and intricate task caching. The diverse requirements of on-vehicle applications, as well as the freshness of dynamic cached information, provide significant challenges for edge servers in efficiently fulfilling energy and latency demands. This work studies a freshness-aware caching-aided offloading-based task allocation problem (FCAOP) in DT-enabled IoV (DTIoV) with Intelligent Reflective Surfaces (IRS) and edge computing. DT is used to accumulate real-time data and digitally depict the physical objects of the IoV to enhance decision-making. A quantum-inspired differential evolution (QDE) algorithm is proposed to reduce the overall delay and energy consumption in DTIoV (QDE-DTIoV). The quantum vector (QV) is encoded to represent a complete solution to the FCAOP. The decoding of the QVs is done using a one-time hashing algorithm. The fitness function is derived by considering delay, energy consumption, and freshness of the tasks. Extensive simulations demonstrate the superiority of QDE-DTIoV over other benchmark algorithms, showing an average latency improvement of 23%-26% and a reduction in energy consumption ranging from 22% to 33%. IEEE
Enhancing the coverage area of the sensing range with the limiting resource is a critical problem in the wireless sensor network (WSN). Mobile sensors are patched coverage holes and they also have limited energy to mo...
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Quality degradation due to the compression and the transmission of images is a significant threat to multimedia applications. Blind image quality assessment (BIQA) is a principal technique to measure the distortion an...
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Classification of brain haemorrhage is a challenging task that needs to be solved to help advance medical treatment. Recently, it has been observed that efficient deep learning architectures have been developed to det...
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This paper proposes a Recursive Neural framework for the clever mindset evaluation of network interference customers. Our technique builds on previous work achieved in sentiment analysis using extracting a person'...
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ISBN:
(纸本)9798350383348
This paper proposes a Recursive Neural framework for the clever mindset evaluation of network interference customers. Our technique builds on previous work achieved in sentiment analysis using extracting a person's man or woman mindset from complicated and incomplete statistics streams. The framework, to begin with, gets the sentiment layers based on consumer interactions from the datasets, after which it integrates this fact with various Recursive Neural networks to seize the sentiment of a single user. The community extracts capabilities associated with the user and learns to distinguish between the behaviors of two users inside the community. Once the community is educated on the datasets, it may classify the sentiment of users based on various contextual cues. We evaluated our framework through crowd-sourced sentiment annotation datasets from a web forum, and it confirmed superior overall performance than different present approaches. We proposed a Recursive Neural framework that utilizes contextual schemas and sentiment to analyze user attitudes and behaviors for community interference scenarios. It can open up promising new opportunities for observing consumer mindset and behavior in online networks. This paper offers a recursive neural framework for competent mindset evaluation of network interference customers. Recursive Neural Networks, broadly carried out in natural language processing responsibilities with sentiment analysis, combine word embeddings with a recursive architecture to gain a perception of the syntactic shape of sentences. On this, look at the Recursive Neural Network (RNN) architecture tailored to research the sentiment mindset of community interference users. The information amassed from Twitter, Weibo, and different open-supply platforms had been pre-processed using the frequency inverted report frequency technique before constructing an RNN for its modeling. Checks at the built community proved that the proposed model furnished pleasant c
This paper proposes a paradigm shift from the current wave of Internet of Musical Things (IoMusT) research, which is mostly centered on technological development, towards the new wave of the Internet of Musical Things...
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As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy conce...
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As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.
this paper investigates the potential of using time collection evaluation to detect Merkel cellular Carcinoma (MCC) early. MCC is a competitive shape of pores and skin. Early detection is critical to the growing survi...
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Nowadays,the COVID-19 virus disease is spreading *** are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited *** diagnose the presence of disease from radiological images,auto-mated ...
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Nowadays,the COVID-19 virus disease is spreading *** are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited *** diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are *** enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting *** most common symptoms of COVID-19 are fever,dry cough and sore *** symptoms may lead to an increase in the rigorous type of pneumonia with a severe *** medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death ***,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and *** approach integrates the union of deep features with the help of Inception 14 and VGG-16 *** last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of *** the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is *** experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.
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