The escalating demand for skilled IT professionals underscores the increasing significance of the recruitment process. Traditional methods often fall short in identifying individuals poised for success in the dynamic ...
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The study investigates the increasing demand of online learning as a means of addressing education issues in the context of the COVID-19 epidemic. Online learning requires several adaptations for teaching methods, lea...
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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
Storyboards comprising key illustrations and images help filmmakers to outline ideas,key moments,and story events when filming *** by this,we introduce the first contextual benchmark dataset Script-to-Storyboard(Sc2St...
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Storyboards comprising key illustrations and images help filmmakers to outline ideas,key moments,and story events when filming *** by this,we introduce the first contextual benchmark dataset Script-to-Storyboard(Sc2St)composed of storyboards to explicitly express story structures in the movie domain,and propose the contextual retrieval task to facilitate movie story *** Sc2St dataset contains fine-grained and diverse texts,annotated semantic keyframes,and coherent storylines in storyboards,unlike existing movie *** contextual retrieval task takes as input a multi-sentence movie script summary with keyframe history and aims to retrieve a future keyframe described by a corresponding sentence to form the *** to classic text-based visual retrieval tasks,this requires capturing the context from the description(script)and keyframe *** benchmark existing text-based visual retrieval methods on the new dataset and propose a recurrent-based framework with three variants for effective context *** experiments demonstrate that our methods compare favourably to existing methods;ablation studies validate the effectiveness of the proposed context encoding approaches.
The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in *** malicious applications have become a serious concern to the...
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The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in *** malicious applications have become a serious concern to the security of Android *** address this problem,researchers have proposed several machine-learning models to detect and classify Android malware based on analyzing features extracted from Android ***,most existing studies have focused on the classification task and overlooked the feature selection process,which is crucial to reduce the training time and maintain or improve the classification *** current paper proposes a new Android malware detection and classification approach that identifies the most important features to improve classification performance and reduce training *** proposed approach consists of two main ***,a feature selection method based on the Attention mechanism is used to select the most important ***,an optimized Light Gradient Boosting Machine(LightGBM)classifier is applied to classify the Android samples and identify the *** feature selection method proposed in this paper is to integrate an Attention layer into a multilayer perceptron neural *** role of the Attention layer is to compute the weighted values of each feature based on its importance for the classification *** evaluation of the approach has shown that combining the Attention-based technique with an optimized classification algorithm for Android malware detection has improved the accuracy from 98.64%to 98.71%while reducing the training time from 80 to 28 s.
This research offers a new perspective on predicting the activity of the HIV virus from the Drug Therapeutics program (DTP) Antiviral Screen by using the molecular data represented in SMILES notation. The topic has si...
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Focusing on feature importance, this study utilized the XGBoostClassifier to predict NBA player salaries within categorized salary bands. Oversampling techniques ensured balanced class representation, while feature im...
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Graph is a powerful sparse data structure that intuitively represents entities and their *** graph traversal algorithms such as Breadth-First Search(BFS),Single-Source Shortest Path(SSSP),PageRank,and Weakly Connected...
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Graph is a powerful sparse data structure that intuitively represents entities and their *** graph traversal algorithms such as Breadth-First Search(BFS),Single-Source Shortest Path(SSSP),PageRank,and Weakly Connected Components(WCC)have extensive applications in social network analysis,risk management for finance,and recommendation ***,graph processing in CPUs and GPUs is not very efficient due to its irregular memory *** people have proposed software approaches to speed up graph processing,such as PowerGraph,PowerLyra,and Shentu,which address load imbalance issues by replicating high-degree *** and GridGraph attempt to improve memory access locality by scanning the edge list of graphs while localizing the range of vertices accessed in a *** and Gemini provide adaptive dual compute modes(bottom-up and topdown),which are particularly effective for BFS-like algorithms such as BFS and ***,pure software approaches have their limitations,and it is desired to see how hardware could be employed to accelerate graph processing.
A bank's marketing campaign execution is very important. Undoubtedly, a well-crafted marketing plan will contribute to the bank's increased revenue. Marketing is crucial because it can be used to connect with ...
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In the digital era and the evolution of social media platforms like TikTok, understanding the factors influencing content virality has become increasingly crucial. Therefore, this research aims to delve into the music...
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