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Evaluation of Classification Techniques for News Data

Evaluation of Classification Techniques for News Data

引用

2025 IEEE international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Thanusree, Uppalapati Gatla, Anitha Abhishek, Baja Jason Kiran, Duvvapu Sai Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798331529833

In the digital world, there is a large amount of online news sources which creates a problem to users to navigate and get the updated information from the News content management. This is a machine learning project which is creating a way to allow articles to be intelligently categorized, so it's simpler to locate those news pieces that are important to you. Additionally, the scalability of each approach will be investigated to determine the suitability for processing large scale news datasets. The purpose of our approach is to classify news article to a relevant topic such that the news articles are grouped and users have a better experience in news platform. Using these performance metrics, this technology has real potential to streamline news management and increase user engagement to the next level. Furthermore, the project also investigates the idea of leveraging natural language processing (NLP) methods to further augment classification accuracy and to better extract the true semantic meaning of the news articles. We preprocessing the data by using natural language processing (NLP) techniques such as cleaning, tokenization, lemmatization and word embedding using TF- IDF vectorizer. Then we split the data and train an ANN and Gaussian Naive Bayes. Overall, this work was meant to make towards the development of a safe and effective device for automated grouping of on the web news articles so that it will certainly be simpler for individuals to access appropriate information in the dense environment of online media. © 2025 IEEE.

关键词： Semantics

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Development of Digital Telecommunication to Analyse and Store Complex media Data: A Way to Data Engineering 1

Development of Digital Telecommunication to Analyse and Stor...

引用

1st international conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Pachigolla, Yesu Ratnam Pflugerville TX United States

ISBN: (纸本)9798331518578

In the era of digital world data flows are saturated with development of universal multifunctional system to solve problems and to optimise the computing resources. The information system is highly loaded with modern data and large number of resources. The user request and heterogeneity of the incoming streams can be evaluated using different types of multimedia services and its requirement for computing resources and its performance with the entire data. The incoming flow of data heterogeneity is considered as the distinctive feature of request in the modern information system to support different types of multimedia services in single platform. Large volumes of data and data heterogeneity Creates numerous problems related to data storage security and the speed of digital system. To address these challenges artificial intelligence technology can be used for execution of digital telecommunication complex for processing and storing the dynamic flow of data that are in multi format. The prospects and trends can be identified to develop these models based on the perspective characteristics. The development of digital communication with multi object analytic system for storing and analysing complex data with data engineering. An fuzzy based model is used for data processing with enhanced accuracy of 98%. © 2025 IEEE.

关键词： Data Engineering data flow data storage digital telecommunication Fuzzy model processing

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Inside and Inside: Efficient Anomaly Detection by Fully Capturing the Detailed Dynamics

Inside and Inside: Efficient Anomaly Detection by Fully Capt...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Tang, Ziyu Zhou, Xiren Chen, Ao Liu, Shikang Wei, Chuyang Chen, Huanhuan School of Computer Science and Technology University of Science and Technology of China Hefei China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China

ISBN: (纸本)9798350368741

Anomaly detection in sequential signals is gaining prominence, especially with limited training data and timeliness requirements. Fully extracting the data-inside changing information, we propose a novel wavelet-Enhanced Reservoir Computing framework (WE-Res). Our framework uses Discrete wavelet Transform (DWT) to decompose signals for multi-level detail and trend extraction recursively. Each level is fitted using an Echo State Network (ESN) respectively, extracting its inside dynamic features into fitted models. Integrating these dynamic features creates a "multi-level dynamic feature" that enhances signal representation, aiding in distinguishing between normal and anomalous patterns. We further introduce a dual-objective optimization to refine ESNs' reservoirs, increasing the fitting accuracy and improving category discrimination among the captured features. Validation on real-world data confirms our method's effectiveness, especially in data-limited scenarios and less training time compared to recent baselines. © 2025 IEEE.

关键词： Anomaly detection Dynamic Feature Model-Based Methods Reservoir computing Time-series signal

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Improved Xception with Local Dense Connections and Transition Layer for Facial Expression Recognition 24

Improved Xception with Local Dense Connections and Transitio...

引用

3rd international conference on Artificial Intelligence and Intelligent information processing, AIIIP 2024

作者： Zhang, Zhiping Abdul-Rahman, Shuzlina Sabri, Norlina Mohd College of Computing Informatics and Media Universiti Teknologi MARA Selangor Malaysia College of Computer and Mathematics Xinyu University Jiangxi Xinyu China Universiti Teknologi MARA Cawangan Terengganu Kuala Terengganu Malaysia

ISBN: (纸本)9798400707308

Traditional deep convolutional neural networks are used for facial expression recognition, which makes the number of neurons and parameters huge, wastes computing resources, and even causes problems such as overfitting and network degradation. Meanwhile, single-scale expression features cannot describe rich facial expression information and are not evenly distributed on face images. We proposed an improved Xception method based on the original Xception network, it retains the depth-separable convolution and residual structure, adds multi-scale convolution and local dense connection. Our proposed method achieved an accuracy rate of 89.54% on the FER2013 dataset, strengthened feature reuse, and enhanced generalization ability. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Delving Into Coarse-Fine Feature Interaction Alignment for UAV Object Detection

Delving Into Coarse-Fine Feature Interaction Alignment for U...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Bi, Yanchao Ning, Yang Nie, Xiushan School of Computer Science and Technology Shandong Jianzhu University Jinan China

ISBN: (纸本)9798350368741

Due to limited features and dense object layouts, object detection in UAV images is challenging. Given that existing feature fusion methods have not fully explored the relationship between fine- and coarse-grained features, direct feature fusion can result in poor correlation between them, hindering the representative capability of fine-grained semantic information. To alleviate this issue, we introduce a method of Coarse-fine Feature Interaction Alignment (CFIA), which enhances the correlation between coarse-grained and fine-grained features across multi-scale feature maps through their interactive alignment. Firstly, we present the wavelet-based High-frequency Preserving Down-sampling (WHPD), utilizing wavelet transform to extract high-frequency information to enhance object boundaries, minimizing crucial fine-grained information loss. Secondly, we propose the Feature Refinement and Interaction Alignment Strategy (FRIAS), which achieves feature interaction alignment by establishing the association of feature maps between coarse-grained and fine-grained features. This enhances the representative capability of feature maps at various scales for detecting small objects. Extensive experiments on the VisDrone, CARPK, and Drone-vs-Bird datasets have demonstrated the effectiveness of the CFIA method, which is highly competitive with state-of-the-art methods. The code is available at https://***/b-yanchao/***. © 2025 IEEE.

关键词： Coarse-fine-grained feature alignment Drone-view small object detection Fine-grained information mining

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Language-Guided Semantic Alignment for Point Cloud-Based 3D Scene Graph Generation

Language-Guided Semantic Alignment for Point Cloud-Based 3D ...

引用

2025 international conference on Electronics, information, and Communication, ICEIC 2025

作者： Lee, Suhyeon Jeong, Jinwoo Jeong, Taehyun Kim, Sungjei Intelligent Image Processing Research Center Korea Electronics Technology Institute Seongnam Korea Republic of School of Computer Science and Engineering Korea University of Technology and Education Cheonan Korea Republic of

ISBN: (纸本)9798331510756

In this paper, we propose a novel model called LAP, Language-guided semantic Alignment for Point cloud-based 3D semantic scene graph generation. Point cloud-based 3D scene graph generation is the task of predicting the category of each instance present in a 3D point cloud scene and the relationships between them. Our model leverages text embeddings for objects, predicates, and triplets during network training, enabling the network to generate reliable semantic scene graphs. To this end, we introduce three loss functions that leverage text embed dings to semantically align the features for scene graph prediction. Comparative experiments with previous methods on the 3DSSG dataset demonstrate the effectiveness of the LAP model. © 2025 IEEE.

关键词： Cloud platforms

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Identification of Negative Intent in Social media Posts Using Natural Language processing 2

Identification of Negative Intent in Social Media Posts Usin...

引用

2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Tiwari, Priti Verma, Parul HarshDev AIIT Amity University Lucknow India Computer Science and Engineering Department Babu Banarasi Das University Lucknow India

ISBN: (纸本)9798331530389

Daily content viewing and/or posting on social media is very common and influences people across all geographies using numerous strategies and ways. A number of daily activities over web platforms like YouTube in terms of posting, watching, and commenting on videos. This is a report that contains a quickly expanding number of online channels where users post, jot down, and comment on a wide range of topics. NLP is applied to quickly take information from online or web content, that will facilitate your business in very different and numerous ways. Customer-specific data can be easily extracted using NLP, and this data can then be used to create advertising campaigns that are specifically targeted to your customers. This assists in introducing your company to the appropriate target market, which is more likely to convert. The paper will perform the critical analysis of versatile methodologies adapted by various researchers for negative intent identification in social networking sites. The paper will also try to observe the challenges associated with the same. This paper will also analyze the usage of NLP in identifying the negative intent in social media posts. © 2025 IEEE.

关键词： Tweets

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Fake news detection using natural language processing

Fake news detection using natural language processing

引用

2025 international conference on computer, Electrical and Communication Engineering, ICCECE 2025

作者： Varma, M. Jaswanth Rohit, M. Sai Selvi, G. Sabeena Gnana Sathyabhama institute of science and technology Department of Cse Chennai India

ISBN: (纸本)9798331513894

Fake news is rampart in the society of the current country and has caused the society to distrust the media, fake news is chosen over real news articles. As a result in the current paper, we discuss the application of natural language processing methods in the identification of fake news on the different platforms. Here we present an unobtrusive framework to automatically interpret news articles as real or fake based on supervised learning with the help of feature extraction from natural language processing. This method contains features based on textual elements which include syntactic features, semantic features and features that speaks about the sentiment of the given content in detail. We enhance feature representation by incorporating pre-trained word embeddings and transformer-based models such as BERT which relieves the amount of structural design and hence, results in better classification accuracy. The experiments we want to propose rely on a dataset containing a set of different news sources;to provide a more realistic scenario, when tuning the model, we use a balanced combination of real news and fake ones, using made up stories. To assess the effectiveness of the proposed framework, we employ tools for performance assessment, namely: precision, recall and F1 score to detect fake information. This result also shows the importance of context and shows how NLP techniques can distinguish between two forms of language use that typically define fake news. In addition, we explain how our work relates to prior research on media literacy and the creation of algorithms for journalists and non-journalists alike. Moreover, our research contributes to the stream of studies that aim at applying NLP for enhancing the effectiveness of combating fake news and the emergence of the society that is better equipped to function within the information logistics of the digital world. All in all, the present overall study focuses on whether advanced language processing tools rem

关键词： Semantics

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Language technology-based Political Microtargeting Services 10th

Language Technology-based Political Microtargeting Services

引用

10th international conference on Technologies and Innovation

作者： Garcia-Diaz, Jose Antonio Marin-Perez, Maris Jose Alcaraz-Marmol, Gema Almela, Angel Minarro-Gimenez, Jose Antonio Garcia-Sanchez, Francisco Univ Murcia Murcia 30003 Spain Univ Castilla La Mancha Ciudad Real 13071 Spain

ISBN: (纸本)9783031757013;9783031757020

In recent times, political behavior, from the act of voting to the participation of citizens in politics, has changed significantly. The proliferation and growth of information and Communication Technologies (ICTs) has provided new and powerful tools to all stakeholders. In particular, social media allow a two-way communication channel between political parties and the electorate. Under these circumstances, accurate segmentation of electoral markets is essential for the development of campaign messages. To enable personalized one-to-one dialogue, it is necessary to characterize each user. However, this poses two major challenges. On the one hand, the degree of subjectivity in the political domain is difficult to determine because a fact can be considered positive or negative depending on the point of view. On the other hand, political polarization and partisanship, which refers to the fact that citizens are strongly biased in favor of certain political parties while strongly disagreeing with others. The goal of this work is to integrate and validate some of the Natural Language processing (NLP) technologies developed and tested by the participating researchers in previous projects for the deployment and optimization of a commercial software platform for political microtargeting through author and user profiling.

关键词： Natural Language processing Subjective Language Author Profiling

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Maintaining Privacy in Contact Tracing Systems for Pandemics Through Edge Computing 1

Maintaining Privacy in Contact Tracing Systems for Pandemics...

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1st international conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： Tejaswini, Virupakshi Sirisala, NageswaraRao K.S.R.M. College of Engineering Department of Computer Science and Engineering A.P Kadapa India

ISBN: (纸本)9798350355611

Data processing and publication are crucial today with the advancements in technologies such as edge computing and IoT(Internet of Things). A vast volume of data is being generated by internet-connected devices, often on a continuous basis. The COVID-19 pandemic data exemplifies this trend of constant data flow, as the global medical emergency has necessitated ongoing data collection. This data must be processed and analyzed, and subsequently published, while ensuring personal privacy is upheld. Edge computing devices can handle various tasks like data preprocessing and analysis in any setting. It is crucial to process data before uploading it to the cloud and sharing it with advisories or media outlets, as improper handling could lead to privacy breaches. To protect personal privacy, measures such as differential privacy techniques are implemented to protect data. Edge computing technologies offer several advantages, including efficient data processing close to the origin to minimize internet data transmission capacity use and enhanced security for confidential information on public cloud infrastructures. © 2025 IEEE.

关键词： Data Sharing Differential Privacy (DP) Edge Computing Individual Privacy

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