With the prevalence of artificial intelligence, people collect data through numerous sensors and use machine learning to create models for intelligent services. However, data privacy and massive data issues are raised...
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Speaker identification using audio data is quite challenging because of inherent differences between people, ambient noise, and variable recording conditions. Although the classical deep learning methods are effective...
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The growth of the internet and technology has had a significant effect on social *** information has become an important research topic due to the massive amount of misinformed content on social *** is very easy for a...
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The growth of the internet and technology has had a significant effect on social *** information has become an important research topic due to the massive amount of misinformed content on social *** is very easy for any user to spread misinformation through the ***,misinformation is a problem for professionals,organizers,and ***,it is essential to observe the credibility and validity of the News articles being shared on social *** core challenge is to distinguish the difference between accurate and false *** studies focus on News article content,such as News titles and descriptions,which has limited their ***,there are two ordinarily agreed-upon features of misinformation:first,the title and text of an article,and second,the user *** the case of the News context,we extracted different user engagements with articles,for example,tweets,i.e.,read-only,user retweets,likes,and *** calculate user credibility and combine it with article content with the user’s *** combining both features,we used three Natural language processing(NLP)feature extraction techniques,i.e.,Term Frequency-Inverse Document Frequency(TF-IDF),Count-Vectorizer(CV),and Hashing-Vectorizer(HV).Then,we applied different machine learning classifiers to classify misinformation as real or ***,we used a Support Vector Machine(SVM),Naive Byes(NB),Random Forest(RF),Decision Tree(DT),Gradient Boosting(GB),and K-Nearest Neighbors(KNN).The proposed method has been tested on a real-world dataset,i.e.,“fakenewsnet”.We refine the fakenewsnet dataset repository according to our required *** dataset contains 23000+articles with millions of user *** highest accuracy score is 93.4%.The proposed model achieves its highest accuracy using count vector features and a random forest *** discoveries confirmed that the proposed classifier would effectively classify misinformat
The sub-6 GHz 5G band has opened doors for a seamless and high-data-rate communication system connecting multiple devices. Multiple-input/multiple-output (MIMO) antennas are the backbone of such reliable communication...
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CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose ***(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information....
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CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose ***(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance ***,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution ***(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between *** problem can be overcome by the use of Wrappers as they select better features by accounting for test and train *** aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between *** proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)*** methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.
The growing advancement of different jobs, skills and businesses has stimulated the spread of diseases. Most of the time, these diseases can cross countries’ borders and spread at a very rapid rate. Avoiding and dete...
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作者:
Yesugade, KiranJadhav, Rohini
College of Engineering Department of Computer Engineering Pune India
College of Engineering Department of Information Technology Pune India
This work investigates the use and assessment of ResNet-50 and VGG16 deep learning models for detecting deepfake images using the Labelled Faces in the Wild (LFW) dataset. The methodology encompassed thorough preparat...
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Sign language is the ancient way of communication which was found before the vocal communication. It bridges the gap between people who are deaf and it overcome the barrier of language difference. Many people learn si...
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Interconnection of all things challenges the traditional communication methods,and Semantic communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic...
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Interconnection of all things challenges the traditional communication methods,and Semantic communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.
With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LL...
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