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A Survey on Fake News Detection Using Multivariate Feature Selection and Hybrid Deep Learning Approach

A Survey on Fake News Detection Using Multivariate Feature S...

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AIML-Applications for Engineering & technology (ICAET), international conference on

作者： Aditya Kumar Dongre G. Kalaiarasi Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Fake news detection has emerged as a critical challenge in the digital age, where misinformation spreads rapidly across social media and news platforms. This survey explores the efficacy of using multivariate feature selection combined with hybrid deep learning approaches to enhance the accuracy and robustness of fake news detection systems. The paper reviews various feature selection techniques, including chi-square, information gain, and correlation-based methods, that help in identifying the most relevant features for detecting fake news. These techniques are crucial in reducing dimensionality, improving processing time, and enhancing the performance of deep learning models. Furthermore, the study examines hybrid deep learning frameworks, which integrate multiple architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers to capture both linguistic and contextual information from news data. This survey provides a comprehensive analysis of recent advancements, highlighting key research contributions, datasets, and evaluation metrics used in fake news detection. The integration of multivariate feature selection with hybrid deep learning models offers a promising approach to addressing the complexities of fake news identification. Finally, the paper outlines the challenges and future research directions in improving model generalization, handling adversarial content, and adapting to evolving patterns of misinformation.

关键词： Deep learning Surveys Adaptation models Accuracy Social networking (online) Computational modeling Feature extraction Transformers Robustness Fake news

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Quantum Influence on Social media Content: Employing Machine Learning for Sentiment Analysis 1st

Quantum Influence on Social Media Content: Employing Machine...

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1st international conference on Machine Learning Algorithms, ICMLA 2024

作者： Lateshwari Bansal, Sushil Kumar Department of Computer Science Maharaja Agrasen Institute of Technology Maharaja Agrasen University Atal Shiksha Kunj HP Solan Kalijhanda Barotiwala174103 India

ISBN: (纸本)9783031758607

The application of natural language processing has proven to be particularly advantageous in the realm of sentiment analysis, particularly when dealing with vast and intricate amounts of unstructured data found within social media platforms. These insights contribute to making informed decisions. Among its multifaceted uses, social media serves as platform for individuals to articulate themselves through tweets and posts. The sentiment of written content, specifically within the realm of social media data, holds the potential to be dissected to extract opinions, emotions, and significant perspectives. Effectively assessing sentiment in publicly available social media data faces challenges from both theoretical and technological sources. While various methodologies have been developed over time, their effectiveness has been constrained by their focus on small datasets, rendering them inadequate in addressing the aforementioned complexities optimally. The recommended approach offers a solution to these challenges, encompassing critical facets such as data collection, feature encoding, feature selection, data pre-processing, and classification, as previously delineated. Notably, the feature encoding stage adopts a hybrid technique that combines the incorporation of bi-gram and tri-gram words. Rigorous testing conducted across multiple benchmark datasets to comprehensively evaluate the performance of the proposed framework. Significantly, the suggested method not only delivers comparable outcomes but, in various instances, superior results while necessitating less intricate computational processes. The average accuracy results attained using the multilayer perceptron neural network ranged from 89 to 91. The methodologies presented in paper are poised to substantially elevate the efficacy of sentiment analysis across diverse blog and social media content. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tweets

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Channel and space-based joint rate allocation algorithm

Channel and space-based joint rate allocation algorithm

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dayong Wang Chao Yuan Yu Sun Xin Lu Hui Guo Frederic Dufaux Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications China Department of Computer Science University of Central Arkansas Faculty of Computing Engineering and Media (CEM) De Montfort University UK Université Paris-Saclay CNRS CentraleSupélec Laboratoire Des Signaux et Systèmes France School of Information and Communication Engineering University of Electronic Science and Technology of China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Rate control is a critical component for image and video compression Particularly under limited network bandwidth conditions, bitrate control is essential to ensure efficient image transmission by effectively allocation channel resources. In this research, since both Channel and Spatial have relationship with rate allocation, we first propose a joint Channel-wise and Spatial-wise Quantization scheme to determine optimal quantization parameters. Subsequently, we develop a quantization step estimation network to obtain parameters to efficiently allocate rate according to target rate. Experiments demonstrate that our algorithm significantly improve compressed image quality with minimal bitrate distortion and achieve accurate rate control with nearly 3% average bitrate error.

关键词： Image quality Quantization (signal) Image coding Bit rate Signal processing algorithms Estimation Rate-distortion Video compression Resource management Speech processing

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Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks with Artificial Hummingbird Algorithm for Social media Content Classification and Community Detection

Stereoscopic Gated Graph Scalable Quantum Capsule Convolutio...

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Machine Learning and Autonomous Systems (ICMLAS), international conference on

作者： A. Elumalai S. Rukmani Devi D. Rosy Salomi Victoria A. Jagadeeswaran Aparajita Mukherjee K. Vinoth Department of Mathematics R.M.K. Engineering College Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Information Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Annapoorana Engineering College Salem Tamil Nadu India Department of Computer Science and Engineering Institute of Engineering and Management(IEM) University of Engineering and Management Kolkata West Bengal Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Social media Content Classification and Community Detection (SMCCCD) classify content and identify communities through deep learning, and NLP. Traditional models are weak in scalability and interpretability. To solve all these problems, this paper introduces a Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks with Artificial Hummingbird Algorithm (SGGSQCCN2Nets+AHA) for social content classification and community detection. Data inputs are accessed from the Twitter dataset and are initially preprocessed by the Natural Language (NL) method. After preprocessing, content classification and community detection are done using the Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks (SGGSQCCN2Nets) and optimization is then performed with the Artificial Hummingbird Algorithm (AHA). The performance of the new SGGSQCCN2Nets+AHA model is tested on a Twitter dataset with a high accuracy rate of 99.9% and specificity of 99.8%. The proposed model is developed by utilizing Python programming language. The outcome of the proposed SGGSQCCN2Nets+AHA model enhances the performance of content classification and improves user community detection accuracy. It is effective in addressing scalability issues, obtaining complex features, and offering more interpretability. The model is also resilient against noisy data dynamically adapts to evolving social media trends and outperforms traditional deep learning techniques in real-time analysis.

关键词： Adaptation models Accuracy Social networking (online) Stereo image processing Scalability Heuristic algorithms Blogs Logic gates Classification algorithms Convolutional neural networks

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Secure Resource Allocation via Constrained Deep Reinforcement Learning 6th

Secure Resource Allocation via Constrained Deep Reinforceme...

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6th international conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Sun, Jianfei Gao, Qiang Wu, Cong Li, Yuxian Wang, Jiacheng Niyato, Dusit School of Computing and Information Systems Singapore Management University Singapore188065 Singapore School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu611130 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9789819645657

The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios-including different task loads, data sizes, and MEC capacities-show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. These enhancements highlight SARMTO ’s potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Linguistic Resources for Extremism Detection on Social media 1st

Linguistic Resources for Extremism Detection on Social Media

引用

1st international conference on Computing and Emerging Technologies, ICCET 2023

作者： Hussain, Muhammad Anwar Shahzad, Muhammad Khurram Sulaiman, Sarina School of Computing Universiti of Teknologi Malaysia Johor Skudai Malaysia Department of Data Science University of the Punjab Lahore Pakistan

ISBN: (纸本)9783031776168

Social networks, such as Facebook, Twitter and Youtube, are becoming robust sources that radical groups use to spread the propaganda of extremist group to gather resources for their cause. Therefore, locating radical social media content has become an priority task for counter-terrorism agencies, technology companies and governments. This study has employed a systematic literature review protocol to identify the publicly available corpora that can be used for training machine learning techniques for extremism detection in text. Furthermore, the snowballing approach is employed to identify the studies that have used these corpora for extremism detection. The identified datasets and the studies have been identified to develop an understanding of landscape of extremism detection. Based on the observations we have developed recommendations for processing this important area of research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tweets

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Improving Stress Detection with Synthetic Datasets: GPT-4o-Mini and Transformer Model Evaluation

Improving Stress Detection with Synthetic Datasets: GPT-4o-...

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17th Asian conference on Intelligent information and Database Systems, ACIIDS 2025

作者： Badran, Nabeel Le, John Le, Thanh Uchiya, Takahiro School of Computing and Information Technology University of Wollongong Northfields Avenue WollongongNSW2522 Australia School of Business Faculty of Business and Law University of Wollongong Wollongong Australia Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya Japan

ISBN: (纸本)9789819660070

The rise of mental health issues as a public health concern necessitates efficient detection methods. Social media is now an integral part of daily life, where users frequently share stress-related experiences. This study investigates synthetic data generation techniques to expand datasets and improve the accuracy of stress analysis in social media posts using machine learning models. Using the OpenAI GPT-4o-mini model through its API, we implement two text augmentation techniques, paraphrasing and inspirational augmentation, generating diverse datasets with original, paraphrased, and inspirationally augmented texts. Evaluations on BERT and DistilBERT show that the combined dataset led to the best result, with BERT achieving an accuracy of 84.74%, compared to 73.94% with the baseline. Our experimental evaluation, conducted on real-world user posts, demonstrates that synthetic datasets significantly improve stress analysis accuracy, highlighting its potential for mental health detection in social media. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Natural language processing systems

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An Approach for Aspect Based Sentiment Analysis Using Multivariate Feature Extraction and Machine Learning

An Approach for Aspect Based Sentiment Analysis Using Multiv...

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AIML-Applications for Engineering & technology (ICAET), international conference on

作者： Priyanka Pravin Patil Sunil Gupta School of Computer & System Sciences JNU Jaipur India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Aspect-based sentiment analysis (ABSA) is a nuanced field within natural language processing that focuses on extracting sentiment information at a granular level, targeting specific aspects of entities within a text. This paper presents an innovative approach to ABSA by leveraging multivariate feature extraction techniques combined with advanced machine learning algorithms. Our method begins with a comprehensive pre-processing phase that includes tokenization, part-of-speech tagging, and dependency parsing to structure the textual data. We then employ multivariate feature extraction techniques, such as term frequency-inverse document frequency (TF-IDF), word embeddings, and syntactic dependency features, to capture the contextual and semantic richness of the text. These features are fed into machine learning models, including support vector machines (SVM), random forests, and deep learning architectures like recurrent neural networks (RNN) etc. We evaluate the performance of these models using a well-defined benchmark dataset, highlighting the effectiveness of combining multivariate features with machine learning in improving the accuracy of aspect-based sentiment classification. Our experimental results demonstrate that the proposed approach significantly outperforms traditional sentiment analysis methods, providing more precise and insightful sentiment evaluations at the aspect level. This advancement holds promising applications in fields such as customer feedback analysis, social media monitoring, and market research, where understanding specific sentiment nuances is crucial.

关键词： Support vector machines Sentiment analysis Recurrent neural networks Machine learning algorithms Social networking (online) Semantics Syntactics Feature extraction Market research Monitoring

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Real-Time Detection of Wrong-Way Vehicles Using YOLO and Image processing Techniques

Real-Time Detection of Wrong-Way Vehicles Using YOLO and Ima...

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Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： J. Jeya A Celin J. Jeswin A. Mohamed Faizaan A.P. Chandru Babu M. Arul Anisto Department of Information Technology Kalasalingam Academy of Research And Education Virudhunagar Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Due to delayed notice and incorrect identification, wrong-way driving is a major traffic safety concern that frequently results in accidents. We describe a computer vision and deep learning based real-time wrong-way vehicle identification system to tackle these issues. To identify cars in video footage and follow their movements within predetermined zones, the system makes use of the YOLO (You Only Look Once) algorithm. It correctly detects instances of driving in the incorrect direction by analysing direction, and it promptly issues warnings by sending out email notifications with pictures of the vehicles and sounding an alert. According to evidence from experiments, the system is a useful tool for increasing road safety because it improves the efficiency of detection, decreases error rates, and speeds up response times.

关键词： YOLO Deep learning Sentiment analysis Accuracy Streaming media Real-time systems Safety Timing Automobiles Time factors

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AKI360: Enabling Highly Interactive 360-degree Video Streaming by Adaptive Keyframe Interval

AKI360: Enabling Highly Interactive 360-degree Video Streami...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Haitao Liu Xinyi Zhang Chuanmin Jia Yanbiao Li Gaogang Xie Chinese Academy of Sciences Computer Network Information Center Beijing China University of Chinese Academy of Sciences Beijing China Peking University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

360-degree video is a panoramic video technology designed to offer audience an immersive visual experience. In Motion Constrained Tile Set (MCTS)-based streaming schemes, the server only updates Field-Of-View (FOV) coordinates when codec generating keyframes, thus the keyframe interval significantly impacts FOV updates and interactivity. However, reducing the keyframe interval poses greater challenges for network transmission and encoding/decoding overhead. To address these issues, we design and implement AKI360, a 360-degree video streaming system with an Adaptive Keyframe Interval (AKI) mechanism along with Quantization Parameter (QP) adjustments. Extensive experiments show that AKI360 reduces the motion stall duration by 31% to 74% and FOV update interval by 78% to 86% comparing with the real-time approaches, and eliminates the blank area in the best-case comparing with the state-of-the-art pre-encoded approach. Meanwhile, AKI360 improves the video quality by up to 0.78 decrease in NIQE while maintaining comparable frame rate.

关键词： Visualization Adaptive systems Quantization (signal) Switches Streaming media Real-time systems Quality assessment Servers Speech processing Video recording

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