Topology is usually perceived intrinsically immutable for a given *** argue that optical topologies do not immediately enjoy such ***'optical skyrmions'as an example,we show that they will exhibit varying text...
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Topology is usually perceived intrinsically immutable for a given *** argue that optical topologies do not immediately enjoy such ***'optical skyrmions'as an example,we show that they will exhibit varying textures and topological invariants(skyrmion numbers),depending on how to construct the skyrmion vector when projecting from real to parameter *** demonstrate the fragility of optical skyrmions under a ubiquitous scenario-simple reflection off an optical *** topology is not without benefit,but it must not be assumed.
Pretrained language models (PLMs) have shown remarkable performance on question answering (QA) tasks, but they usually require fine-tuning (FT) that depends on a substantial quantity of QA pairs. Therefore, improving ...
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In an era dominated by information dissemination through various channels like newspapers,social media,radio,and television,the surge in content production,especially on social platforms,has amplified the challenge of...
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In an era dominated by information dissemination through various channels like newspapers,social media,radio,and television,the surge in content production,especially on social platforms,has amplified the challenge of distinguishing between truthful and deceptive *** news,a prevalent issue,particularly on social media,complicates the assessment of news *** pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources,creating confusion and polarizing *** the volume of information grows,individuals increasingly struggle to discern credible content from false narratives,leading to widespread misinformation and potentially harmful *** numerous methodologies proposed for fake news detection,including knowledge-based,language-based,and machine-learning approaches,their efficacy often diminishes when confronted with high-dimensional datasets and data riddled with noise or *** study addresses this challenge by evaluating the synergistic benefits of combining feature extraction and feature selection techniques in fake news *** employ multiple feature extraction methods,including Count Vectorizer,Bag of Words,Global Vectors for Word Representation(GloVe),Word to Vector(Word2Vec),and Term Frequency-Inverse Document Frequency(TF-IDF),alongside feature selection techniques such as Information Gain,Chi-Square,Principal Component Analysis(PCA),and Document *** comprehensive approach enhances the model’s ability to identify and analyze relevant features,leading to more accurate and effective fake news *** findings highlight the importance of a multi-faceted approach,offering a significant improvement in model accuracy and ***,the study emphasizes the adaptability of the proposed ensemble model across diverse datasets,reinforcing its potential for broader application in real-world *** introduce a pioneering ensemble
Neurological disorders such as Alzheimer’s disease(AD)are very challenging to treat due to their sensitivity,technical challenges during surgery,and high *** complexity of the brain structures makes it difficult to d...
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Neurological disorders such as Alzheimer’s disease(AD)are very challenging to treat due to their sensitivity,technical challenges during surgery,and high *** complexity of the brain structures makes it difficult to distinguish between the various brain tissues and categorize AD using conventional classification ***,conventional approaches take a lot of time and might not always be ***,a suitable classification framework with brain imaging may produce more accurate findings for early diagnosis of *** in this paper,an effective hybrid Xception and Fractalnet-based deep learning framework are implemented to classify the stages of AD into five ***,a network based on Unet++is built to segment the tissues of the ***,using the segmented tissue components as input,the Xception-based deep learning technique is employed to extract high-level ***,the optimized Fractalnet framework is used to categorize the disease condition using the acquired *** proposed strategy is tested on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset that accurately segments brain tissues with a 98.45%of dice similarity coefficient(DSC).Additionally,for themulticlass classification of AD,the suggested technique obtains an accuracy of 99.06%.Moreover,ANOVA statistical analysis is also used to evaluate if the groups are significant or *** findings show that the suggested model outperforms various stateof-the-art methods in terms of several performance metrics.
The South Indian mango industry is confronting severe threats due to various leaf diseases,which significantly impact the yield and quality of the *** management and prevention of these diseases depend mainly on their...
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The South Indian mango industry is confronting severe threats due to various leaf diseases,which significantly impact the yield and quality of the *** management and prevention of these diseases depend mainly on their early identification and accurate *** central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks(CNNs)as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango *** study collected a rich dataset of leaf images representing different disease classes,including Anthracnose,Powdery Mildew,and Leaf *** maintain image quality and consistency,pre-processing techniques were *** then used a customized deep CNN architecture to analyze the accuracy of South Indian mango leaf disease detection and *** proposed CNN model was trained and evaluated using our collected *** customized deep CNN model demonstrated high performance in experiments,achieving an impressive 93.34%classification *** result outperformed traditional CNN algorithms,indicating the potential of customized deep CNN as a dependable tool for disease *** proposed model showed superior accuracy and computational efficiency performance compared to other basic CNN *** research underscores the practical benefits of customized deep CNNs for automated leaf disease detection and classification in South Indian mango *** findings support deep CNN as a valuable tool for real-time interventions and improving crop management practices,thereby mitigating the issues currently facing the South Indian mango industry.
Cloud computing has emerged as a promising mode for storaging vast quantities of big data, which is vulnerable to potential security threats, making it urgent to ensure data confidentiality and integrity auditing. In ...
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Cloud is based on the underlying technology of virtualization. Here, the physical servers are divided into multiple virtual servers. Through the technology of virtualization, each virtual server contains virtual machi...
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Vehicle-to-Everything(V2X) communications will be an essential part of the technology in future autonomous drive decision systems.A fundamental procedure is to establish a robust communication channel between end-to-e...
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Vehicle-to-Everything(V2X) communications will be an essential part of the technology in future autonomous drive decision systems.A fundamental procedure is to establish a robust communication channel between end-to-end *** to the antenna placed at different positions on vehicles,the existing cellular electro-magnetic(EM) wave propagation modelling does not fit properly for V2X direct communication *** order to figure out a feasible understanding of this problem,this paper focuses on the propagation channel analysis in a rural Vehicle-to-Vehicle(V2V) scenario for vehicular communication with antenna position experiments at different *** adopting the ray-tracing algorithm,a rural scenario simulation model is built up via the use of a commercial-off-the-shelf(COTS) EM modelling software package,that computes the path loss received power and delay spread for a given propagation ***,a real-world vehicle measurement campaign was performed to verify the simulation *** simulated and measured receiver power was in good agreement with each other,and the results of this study considered two antenna types located at three different relative heights between the two *** research provides constructive guidance for the V2V antenna characteristics,antenna placement and vehicle communication channel analysis.
Prediction systems are an important aspect of intelligent *** engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the modeling accur...
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Prediction systems are an important aspect of intelligent *** engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the modeling accuracy of the *** belief rule base(BRB)can implement nonlinear modeling and express a variety of uncertain information,including fuzziness,ignorance,randomness,***,the BRB system also has two main problems:Firstly,modeling methods based on expert knowledge make it difficult to guarantee the model’s ***,interpretability is not considered in the optimization process of current research,resulting in the destruction of the interpretability of *** balance the accuracy and interpretability of the model,a self-growth belief rule basewith interpretability constraints(SBRB-I)is *** reasoning process of the SBRB-I model is based on the evidence reasoning(ER)***,the self-growth learning strategy ensures effective cooperation between the datadriven model and the expert system.A case study showed that the accuracy and interpretability of the model could be *** SBRB-I model has good application prospects in prediction systems.
Numerous neural network(NN)applications are now being deployed to mobile *** applications usually have large amounts of calculation and data while requiring low inference latency,which poses challenges to the computin...
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Numerous neural network(NN)applications are now being deployed to mobile *** applications usually have large amounts of calculation and data while requiring low inference latency,which poses challenges to the computing ability of mobile ***,devices’life and performance depend on ***,in many scenarios,such as industrial production and automotive systems,where the environmental temperatures are usually high,it is important to control devices’temperatures to maintain steady *** this paper,we propose a thermal-aware channel-wise heterogeneous NN inference *** contains two parts,the thermal-aware dynamic frequency(TADF)algorithm and the heterogeneous-processor single-layer workload distribution(HSWD)*** on a mobile device’s architecture characteristics and environmental temperature,TADF can adjust the appropriate running speed of the central processing unit and graphics processing unit,and then the workload of each layer in the NN model is distributed by HSWD in line with each processor’s running speed and the characteristics of the layers as well as heterogeneous *** experimental results,where representative NNs and mobile devices were used,show that the proposed method can considerably improve the speed of the on-device inference by 21%–43%over the traditional inference method.
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