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Truth in the Age of Misinformation: A Dual-Approach Model for Improved Fake News Detection

Truth in the Age of Misinformation: A Dual-Approach Model fo...

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2024 international conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Malik, Ishan Singh, Sanjay Kumar Saxena, Pranshu University School of Automation and Robotics Guru Gobind Singh Indraprastha University East Delhi Campus Surajmal Vihar Delhi110092 India Abes Engineering College Department of Information Technology Ghaziabad India

ISBN: (纸本)9798350379716

The inspiration that drives this study is the increase in the prevalence of misinformation, and the need to solve the big challenge of detecting fake news. With the possibility of uncontrolled misinformation disastrously impacting our society, the objective of our study is to make contributions towards the body of knowledge which aims to improve the efficiency and accuracy of current methodologies to detect false information. Although there has been an improvement in the fake news detection systems, their adaptation to real-time applications remains sub-optimal due to the subtle and complex processing involved with handling misinformation. The purpose of this study is to bridge this gap and come up with a better detection model using the Recurrent neural network (RNN) models like Long Short-Term Memory (LSTM) and Bi-directional LSTM (BiLSTM) that will enhance the performance of the existing models, especially in terms of detection accuracy, as the existing systems are weak in the 'disagree' category and mostly misclassify it. We use a mix of machine learning algorithms and natural language processing techniques. Integrating LSTM with BiLSTM will analyze textual content at a granular level to find patterns representing fake news. The preliminary results have indicated that our model is performing significantly better than existing ones, with an overall improvement of up to 86.25% in accuracy [15]. However, in the 'disagree' category, there are a few challenges faced by the system, reflecting the scope for many improvements in the future. Our research has made a valuable contribution to the knowledge and tools in the area of digital information credibility by providing a strong model that improves detection rates of more subtle cues of misinformation thereby, combating the spread of fake news. © 2024 IEEE.

关键词： Long short-term memory

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Communication Assistance for Blind and Deaf using natural language processing

Communication Assistance for Blind and Deaf using Natural La...

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knowledge engineering and Communication Systems (ICKECS), international conference on

作者： V Sangeetha Ishwariya Pani K Yasaswini B Nikita Arpita Bhattacharjee Department of CSE Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Having difficulties like being visually impaired, hard of listening to, dumb are a more amount of difficulty. Technology and innovation have motivated humans to grow to be depending on solace but there exists an underprivileged growth of those who are fighting for finding a creative manner that could make the procedure of conversation less difficult for them. The traditional ways of communication and knowing any information for disabled people is very time consuming as they had to find someone to ask for the information and wait for their response, and accurate answers are not received and language or gestures act as barriers. To overcome these problems, in this paper natural language processing(NLP) and Speech Recognition is used. NLP and Speech recognition eradicate language barriers between the disabled and the non-disabled person. The main objectives is to convert the spoken audio to text format, speech-to-text is used and to convert the typed text to audio format, text-to-speech is used. Speech to text and text to speech features in the website application are faster. This application gives a user friendly platform for the blind and deaf.

关键词： knowledge engineering Technological innovation Accuracy Communication systems Oral communication Digital signal processing People with disabilities

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knowledge Distillation with Source-free Unsupervised Domain Adaptation for BERT Model Compression 26

Knowledge Distillation with Source-free Unsupervised Domain ...

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26th international conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Tian, Jing Chen, Juan Chen, Ning Jiang Bai, Lin Huang, Suqun Guangxi University School of Computer and Electronic Information Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning530004 China Education Department of Guangxi Zhuang Autonomous Region Nanning530004 China

ISBN: (纸本)9798350331684

The pre-training language model BERT has brought significant performance improvements to a series of natural language processing tasks, but due to the large scale of the model, it is difficult to be applied in many practical application scenarios. With the continuous development of edge computing, deploying the models on resource-constrained edge devices has become a trend. Considering the distributed edge environment, how to take into account issues such as data distribution differences, labeling costs, and privacy while the model is shrinking is a critical task. The paper proposes a new BERT distillation method with source-free unsupervised domain adaptation. By combining source-free unsupervised domain adaptation and knowledge distillation for optimization and improvement, the performance of the BERT model is improved in the case of cross-domain data. Compared with other methods, our method can improve the average prediction accuracy by up to around 4% through the experimental evaluation of the cross-domain sentiment analysis task. © 2023 IEEE.

关键词： Distillation

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MEASURING VISION-language STEM SKILLS OF NEURAL MODELS 12

MEASURING VISION-LANGUAGE STEM SKILLS OF NEURAL MODELS

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12th international conference on Learning Representations, ICLR 2024

作者： Shen, Jianhao Yuan, Ye Mirzoyan, Srbuhi Zhang, Ming Wang, Chenguang School of Computer Science Peking University China National Key Laboratory for Multimedia Information Processing Peking University China Peking University Anker Embodied AI Lab China Washington University St. Louis United States

We introduce a new challenge to test the STEM skills of neural models. The problems in the real world often require solutions, combining knowledge from STEM (science, technology, engineering, and math). Unlike existing datasets, our dataset requires the understanding of multimodal vision-language information of STEM. Our dataset features one of the largest and most comprehensive datasets for the challenge. It includes 448 skills and 1, 073, 146 questions spanning all STEM subjects. Compared to existing datasets that often focus on examining expert-level ability, our dataset includes fundamental skills and questions designed based on the K-12 curriculum. We also add state-of-the-art foundation models such as CLIP and GPT-3.5-Turbo to our benchmark. Results show that the recent model advances only help master a very limited number of lower grade-level skills (2.5% in the third grade) in our dataset. In fact, these models are still well below (averaging 54.7%) the performance of elementary students, not to mention near expert-level performance. To understand and increase the performance on our dataset, we teach the models on a training split of our dataset. Even though we observe improved performance, the model performance remains relatively low compared to average elementary students. To solve STEM problems, we will need novel algorithmic innovations from the community. © 2024 12th international conference on Learning Representations, ICLR 2024. All rights reserved.

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A Machine Learning Approach for the Detection of Thoracic Disease using Chest X-ray reports 28th

A Machine Learning Approach for the Detection of Thoracic Di...

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28th international conference on knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Aversano, Lerina Iammarino, Martina Madau, Antonella Montano, Debora Verdone, Chiara Department of Agricultural Science Food Natural Resources and Engineering University of Foggia Foggia71122 Italy Department of Computer Science University of Bari Aldo Moro Bari70121 Italy Department of Engineering University of Sannio Benevento82100 Italy CeRICT scrl - Regional Center Information Communication Technology Benevento82100 Italy

Today, several chest diseases are on the rise and these are often diagnosed through the use of chest X-rays, a common and economical clinical test to perform. This work uses a machine learning approach for the detection of thoracic diseases using chest X-ray reports and involves leveraging algorithms and models to analyze medical imaging data for the presence of various conditions affecting the chest area. Our main goal is to create a predictive model based on textual reports released by radiologists, with the use of natural language processing. The proposed approach aims to facilitate the examination of textual reports written by radiologists, to predict the onset of diseases in patients. Specifically, reports generated by radiologists are meticulously processed and reviewed using the GloVe and LSI models. This analysis allows you to identify the presence of diseases and provides insights into the specific thoracic pathology. The results obtained through the implementation of our approach (accuracy above 96% for the best model) underline the good performance and potential of the developed predictive model. © 2024 The Authors.

关键词： Radiology

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Enhancing natural language processing with Machine Learning for Conversational AI 4

Enhancing Natural Language Processing with Machine Learning ...

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4th international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Bataineh, Abdallah Q. Abu-AlSondos, Ibrahim A. Almazaydeh, Laiali El Mokdad, Soha Salem Allahham, Mahmoud Digital Marketing Department Al-Zaytoonah University of Jordan Amman Jordan IT Management Department American University in the Emirates Dubai United Arab Emirates College of Computer IT American University in the Emirates Dubai United Arab Emirates Sharjah Women’s College Higher Colleges of Technology Sharjah United Arab Emirates Business Administration Department Middle East University Amman Jordan

ISBN: (纸本)9781839539954

This research focuses on improving NLP abilities using machine learning to build intelligent conversation agents. The information fetched through the literature review and the approach to the research methodology includes collecting, processing, and testing the models with big NLP datasets. The research's goal is to intensely improve the quality of conversational AI by fine-tuning state-of-the-art NLP models for a conversational context. This research was conducted in Jordan, where they exploited the region's unique features and dialects for enhanced knowledge of the natural language by AI, thereby improving the possible uses of conversational AI globally. The expected outcomes are twofold: enhanced conversational capabilities for conversational AI and a significant research contribution to the knowledge base, especially in customer service and virtual assistance. The impact of the research is far-reaching as improvements in conversational AI are expected to ease cross-sector operations, improve user experience, and lay a foundation for future developments in language-based AI interaction. The research stands at the cutting edge of technological development in Jordan and may serve as a model for other regional Conversational AI innovations. The use of machine learning combined with NLP is not only moving forward the conversation abilities of artificial intelligence systems, but it is also preparing the ground for next-generation advancements to change the nature of human-computer interaction. The realization of the findings can be instrumental in opening up a whole new era of AI whereby it talks, comprehends, and even satisfies the needs of humans through unparalleled eloquence and accuracy. © The Institution of engineering & Technology 2023.

关键词： Chatbots

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A lightweight biomedical named entity recognition with pre-trained model 3

A lightweight biomedical named entity recognition with pre-t...

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2023 IEEE 3rd international conference on Data Science and Computer Application, ICDSCA 2023

作者： Gou, Yunhe Jie, Cao Nanjing University of Finance and Economics School of Information Engineering Nanjing China

ISBN: (纸本)9798350341546

Biomedical Named Entity Recognition (BioNER) is a specialized subfield of Named Entity Recognition (NER) that focuses on identifying and classifying named entities in biomedical and clinical texts. The goal of BioNER is to extract essential information, such as genes, proteins, diseases, drugs, et al., from scientific literature, electronic health records (EHRs), biomedical databases, and other biomedical text sources. The recognition and classification of these entities are crucial for various biomedical and healthcare-related tasks, including information retrieval, data integration, knowledge extraction, and drug discovery. Traditional BioNER methods typically involve rule-based approaches or machine learning algorithms, et al. These methods have been widely used before the advent of deep learning and transformer-based models. Bidirectional Encoder Representations from Transformers (BERT) is a groundbreaking transformer-based language model. It has revolutionized various natural language processing (NLP) tasks by capturing contextual information and obtain optimal results in multiple benchmarks. A lightweight BioNER optimized model from traditional BERT (LWNER) is proposed in this study, which can capture contextual information and its knowledge transfer from pre-training on large-scale text corpora without relying heavily on feature engineering and handcrafted rules. Fine-tuning BERT on biomedical-specific data helps adapt the model to the nuances and terminology of the biomedical domain. We conduct the method LWNER on BioCreative dataset, BC2GM, BC4CHEMD, BC5CDR, especially the chemical entity in BC5CDR achieve F1- score 91.3%. We construct an online web tool based on LWNER to identify the arbitrary text from scientific literatures for building knowledge graph. © 2023 IEEE.

关键词： BERT BiLSTM BioNER Transformer

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Research on the Text2SQL Method Based on Schema Linking Enhanced 1

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4th international conference on Artificial Intelligence in China, AIC 2022

作者： Tie, Jun Zhu, Boer Sun, Chong Fan, Ziqi College of Computer Science South-Central Minzu University Wuhan430074 China Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises Wuhan430074 China

ISBN: (数字)9789819912568

ISBN: (纸本)9789819912551

In recent years, natural language generation of SQL sentences (Text2SQL) has received a lot of attention as an important research direction natural language processing (NLP). Text2SQL makes it easier for users to query complex databases without learning SQL sentences and the underlying database schema. The current mainstream Text2SQL method is the grammar-based IRNet, which attempts to present an efficient method with explanations from the perspective of constructing reasonable grammars and provides a good solution for solving complex nested queries. Still, it makes simple use of external database ontology knowledge, resulting in natural language problems in which words do not correspond well to tables and columns in the database. To address this problem, a new method that considers the entity relationships between natural language problems and data in the database - SLESQL is proposed, which extends some of the functionality of IRNet by using schema linking enhanced Experiments show that SLESQL achieves 6.8% improvement in accuracy over IRNet on the publicly available dataset Spider. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Database systems

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Advanced Techniques for Chinese Image Captioning: Investigating Attention Mechanisms Based on Object Detection for Chinese Image Caption Generation

Advanced Techniques for Chinese Image Captioning: Investigat...

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Machine Learning and Computer Application (ICMLCA), international conference on

作者： Yongbin Hua Pei'ang Li Xiangjin Zeng Hong Xu School of Computer Science and Engineering Wuhan Institute of Technology Wuhan Hubei China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

The task of image caption generation aims to automatically produce natural language descriptions that match the content of images, integrating the fields of machine vision and natural language processing, which holds significant theoretical and practical value. Inspired by top-down attention mechanisms, this paper proposes an innovative attention model. Utilizing the output of pretrained object detection networks as prior knowledge for images, the model guides the generation of natural language descriptions. By directly incorporating the results of object detection as attention inputs into the text generation network, the model effectively focuses on key descriptive regions of images, thereby significantly enhancing performance. On public Chinese image captioning datasets, this model demonstrates substantial advantages in metrics such as BLEU-4 and METEOR.

关键词： Measurement knowledge engineering Attention mechanisms Computational modeling Machine vision Object detection Computer applications Data models natural language processing Meteors

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A Survey of Character Recognition in Tibetan Historical Documents 4

A Survey of Character Recognition in Tibetan Historical Docu...

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4th international conference on Computer Graphics, Image, and Virtualization, ICCGIV 2024

作者： Gongqu, Zhuome Cairang, Jia Duojie, Cairang Jiayang, Dongzhou Luo, Peng The College of Computer Qinghai Normal University Qinghai Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Qinghai Xining810008 China

ISBN: (纸本)9781510683242

Text recognition for Tibetan historical document images is the automated process of extracting and identifying text from these photos utilizing image processing, computer vision, and natural language processing techniques. It is generally understood to be a method of extracting text from visual data. In order to create editable, searchable, and analytical text forms that readers, researchers, and academics may study, preserve, and pass on, it attempts to analyze, identify, and change the text found in traditional Tibetan historical document images. Recent years have seen significant advancements in the effectiveness and performance of Tibetan historical document image and text identification thanks to the quick development of artificial intelligence technology. However, issues with low image quality, non-standard formatting, and notable font style inconsistencies persist in the field of image recognition of Tibetan historical document texts. As a result, the accuracy and universality of current recognizers are low. First, this article gives a general overview of the basic knowledge of Tibetan historical documents, which are the research objects in this field. This helps to better summarize the work of predecessors and support future *** methods of integrating deep learning and conventional methods for Tibetan historical document image text recognition were then arranged, categorized, summarized, and introduced around the four subtasks of dataset construction, text detection, text recognition, and layout analysis. This served as the basis for sorting through the pertinent statistics and assessment indicators and summarizing the state and advancement of the research. Lastly, future technical development patterns are projected based on the primary issues and problems in this subject that require immediate attention. © 2024 SPIE.

关键词： History

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