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PDF Fusion: Revolutionizing Document Management with Chat Interfaces and Voice Recognition

PDF Fusion: Revolutionizing Document Management with Chat In...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Jyotsna Vilas Barpute Vanita Kshirsagar Sneha Salunke Suraj Vedpathak Krishna Varma Vidit Solanki Dept. Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pimpri Pune

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

“PDF Fusion” introduces an innovative system that integrates PDF handling, chat interfaces, and voice recognition technologies to revolutionize document management. Recent techniques in document management include advanced OCR (Optical Character Recognition) for text extraction, natural language processing for context understanding, and AI-driven summarization tools. Despite these advancements, challenges such as information overload, complex document structures, and limited interactivity persist. Our proposed system addresses these challenges by offering an intuitive and efficient platform for interacting with multiple PDF files. Key features include multiple file management, voice recognition, PDF summaries, and links to relevant Wikipedia and YouTube content in response to user queries. the objective is to enhance productivity and user experience through seamless navigation, comprehensive search capabilities, and interactive communication via chat interfaces and voice commands. Rigorous empirical evaluations and user testing demonstrate the practical usability and effectiveness of our integrated approach. By bridging the gap between traditional document management systems and modern interactive platforms, “PDF Fusion” significantly advances human-computer interaction, contributing valuable insights into optimizing digital document workflows and improving accessibility and usability across various domains.

关键词： Video on demand Optical character recognition Text summarization Speech recognition Portable document format Natural language processing User experience Internet web sites Usability

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Relation Extraction from Unstructured Species Descriptions Using TaxonNERD and Llama 7B

Relation Extraction from Unstructured Species Descriptions U...

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BioInspired processing (BIP), IEEE international conference on

作者： Fabricio Ríos Montero Ervin Rodriguez Villanueva María Auxiliadora Mora Dept. of Computer Science Instituto Tecnológico de Costa Rica Alajuela Costa Rica

ISBN: (数字)9798350353709

ISBN: (纸本)9798350353716

Ontologies have become essential tools in knowledge representation, particularly in organizing information on taxonomy, ecology, and inter-species relationships. their integration with Natural language processing (NLP) methods, such as Named Entity Recognition (NER) and Relation Extraction (RE), has significantly advanced the extraction of relevant ecological data from scientific texts. this research explores the fusion of ontological frameworks and NLP methodologies, focusing on animal feeding relationships. Using a dataset from the National Biodiversity Institute (INBio), we applied TaxoNERD for entity extraction, followed by fine-tuning the LLaMA2-7b model to analyze complex feeding behaviors and predator-prey interactions. Our results show a recall of 0.73 and a precision of 0.68, highlighting the model's effectiveness in capturing feeding relationships. these structured insights into species' diets offer valuable contributions to ecological research and conservation efforts.

关键词： Analytical models Biological system modeling Taxonomy Focusing Named entity recognition Ontologies Predator prey systems Ecology Biodiversity Data mining

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Modulation and Demodulation: A Defense Method Against Weight Bit-Flip Attacks in Floating Point CNN Hardware Accelerators

Modulation and Demodulation: A Defense Method Against Weight...

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IEEE international conference on Electronic information and Communication technology (ICEICT)

作者： Zixiao Wang Ying Zhang Ruhui Ding Shuying Xia Zhiyu Fang Xin Chen College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

A Convolutional Neural Network (CNN) is a highly utilized deep learning model in various domains, including computer vision and natural language processing. Disruption of the network structure of CNN might cause significant losses. One often-used and effective attack method is the bit-flip attack. this approach affects the prediction accuracy of CNN models through carefully manipulating bits, and in certain cases, it can entirely disrupt the model’s ability to make accurate predictions, resulting in random guessing. In this work, we propose a defense strategy to counter bit-flip attacks, aiming to improve prediction accuracy in the occurrence of such attacks. this work involves the implementation of Lenet-5 and Lenet-3D on FPGA devices, along with the addition of proposed modulator and demodulator to these two CNN models. the results demonstrate that the prediction accuracy of the CNN structure is effectively improved when the proposed modulator and demodulator are incorporated, even when subjected to the same bit-flip attack injection rate. By flipping 200 bits per layer, our method can significantly enhance the accuracy of the MNIST dataset from 19% to over 90%. Similarly, the accuracy of the CIFAR-10 dataset can be increased from 12% to 52%. Furthermore, in terms of hardware resource consumption, the proposed security unit comprises less than 3% of the overall system.

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international Seminar on Research of information technology and Intelligent Systems (ISRITI)

作者： Siska Arifiani Andhik Ampuh Yunanto Siti Rochimah Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Engineering Politeknik Negeri Elektronika Surabaya Surabaya Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

the learning plan document is used to address the learning area within the learning environment. It also outlines the methodology to achieve the learning outcomes. Due to the unequal distribution of performance indicators, Indonesia implements a standard to ensure the quality of students from each higher education institution. However, the same study program sometimes does not have the same learning outcomes. Bloom's taxonomy is a hierarchical model that can be used to classify educational learning objectives into several levels of complexity and specificity. there are three domains in learning objectives: cognitive, affective, and psychomotor (not sensory). this Bloom's Taxonomy utilizes specific verbs to develop learning objectives in learning plan documents, or "Rencana Pembelajaran Semester" (RPS) in Indonesia. this research proposes a mechanism to measure the standard of RPS documents. this ensures that the same study program can have aligned learning outcomes based on Bloom's taxonomy standards. Similarity algorithms, such as Jaccard Similarity and TF-IDF Cosine Similarity, will be compared. An efficiency and effectiveness matrix is proposed to measure the best similarity algorithm for checking the standard of RPS documents. A web-based system is also developed to provide features that help align learning plans with the desired cognitive levels in Bloom's Taxonomy. this aims to enhance the effectiveness of the learning experience.

关键词： Text mining Seminars Taxonomy Manuals Machine learning Natural language processing Usability Standards Testing Qualifications

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Expression of Concern for: Analysis of QoS for Conveying Authorisation Based on Internet of things (IoT) in Wireless Sensor Networks (WSN)

Expression of Concern for: Analysis of QoS for Conveying Aut...

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international conference on Smart Structures and Systems (ICSSS)

作者： N. Prakash M. Rajalakshmi R. Nedunchezhian Department of ECE KIT-Kalaignarkarunanidhi Institute of Technology Coimbatore Tamilnadu India Department of Information Technology Coimbatore Institute of Technology Coimbatore Tamilnadu India Department of computer science Engineering Coimbatore Institute of Technology Coimbatore Tamilnadu India

ISBN: (数字)9781728172231

ISBN: (纸本)9781728172286

the headways in remote innovation and computerized gadgets led to some modest gadgets have begun to be utilized in various zones in day by day life. the mentioned gadgets are fit for detecting, calculation and imparting. these devices are commonly made of low power radios, a few shrewd sensors and installed Central processing Units. the mentioned gadgets are utilized for framing remote sensor organize that are important for giving detecting administrations and to screen natural conditions. Parallelly with wireless sensor networks (WSNs), the possibility of web of things has been created. Here the Internet of things (IoT) could be characterized as an interconnection between checking forms and recognizable gadgets inside the web association in detecting. Expanding the battery lifetime and diminishing the power utilization utilizing ultra-low power sensor hubs and vitality gathering frameworks is fundamental to acknowledge IoT gadgets. Around 300 devices are used for the experiment. the basic perspective for decreasing vitality utilization and upgrading system lifetime is advancement of proficient steering calculation. Grouping is said to be a key strategy that is used for upgrading the organize lifetime. In this manuscript an IoT engineering empowering an Internet of things Service Level Agreement (IoT-SLA) accomplishment between an IoT-Service Provider (IoT-SP) and an Internet of things- Client (IoT-C) has been dealt with. A novel system has been proposed for the most reduced layer of the administration level which is based detecting layer. the ingress technique known as the QoS-CA-IoT (QoS for conveying authorization based on IoT).

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Multimodal Learning Data Intelligent Personalized Learning Resource Recommendation System for web-Based Classrooms

Multimodal Learning Data Intelligent Personalized Learning R...

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IEEE international conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Shan Wang Shandong Vocational College of Science and Technology Weifang China

ISBN: (数字)9798350377255

ISBN: (纸本)9798350377262

Traditional recommender systems are difficult to fully explore the complex correlation between multimodal data and cannot meet learners' personalized needs. To this end, this paper proposes an intelligent personalized learning resource recommendation system (MDRS) based on multimodal data, which achieves accurate recommendation of learning resources by integrating advanced technologies such as natural language processing, computer vision, and deep learning, and at the same time, by introducing a multimodal data processing mechanism and personalized knowledge graph construction. First, we introduce a multimodal feature embedding module for the model, which utilizes deep neural networks for feature extraction and embedding of data in different modalities, such as text, image, audio, and video, in order to capture the multidimensional characteristics of resources. Subsequently, through the cross-modal fusion and attention mechanism module, the model performs weighted fusion of multimodal features to strengthen the expression of features related to user learning needs. By dynamically adjusting the weights of each modality, the system is able to automatically focus on the features that are most important for learning resource recommendation and eliminate redundant information. this mechanism ensures the system's adaptive fusion of different modalities, allowing multimodal information to be deeply related at the feature level. Next, we design a personalized knowledge graph module to associate users' learning behaviors with resource knowledge points to provide background knowledge support for the recommendation process, which updates the knowledge graph in real time by tracking learners' learning paths, preferences, and knowledge mastery and optimizes the recommended content based on learners' dynamic needs. Finally, through the recommendation optimization module, the recommendation strategy is dynamically adjusted in combination with user feedback to enhance the learning

关键词： Adaptation models Computational modeling Knowledge graphs Learning (artificial intelligence) Feature extraction Data processing Data models Real-time systems Recommender systems Optimization

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A Finetuned Multimodal Deep Learning Model for Xray Image Diagnosis

A Finetuned Multimodal Deep Learning Model for Xray Image Di...

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international conference on Communications, information System and Computer Engineering (CISCE)

作者： Chen Wei Honghao Wang Zhihao Feng Huafeng Mai Zhengzhou University Zhengzhou China University of Melbourne Beijing China Shenzhen Hui Tong International School Shenzhen China University of California San Diego San diego United States

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Generative conversational AI has shown significant potential in aiding biomedical professionals, yet existing research predominantly targets single-mode textual communication. While multimodal conversational AI has advanced quickly through utilizing vast numbers of image-text pairs available online, these broad-domain vision-language models are still insufficiently refined in their ability to comprehend and discuss biomedical imagery. this article primarily addresses question-answering (QA) for medical imaging data and cancer diagnosis. We constructed a relevant instruction-based QA dataset and performed directive fine-tuning on the Qwen-VL 7B model. thanks to the use of QLora technology, our model can be trained quickly on a Nvidia RTX 4090 graphics card and achieves better results compared to other multimodal models. the experiments showed that the Qwen- VL-7B model has best Rouge-1, Rouge-2, Rouge-L score compared to other models, meaning its highly potential for medical question-answering application.

关键词： Training Measurement Visualization Biological system modeling Graphics processing units X-rays Question answering (information retrieval)

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Deep Learning Models for Understanding Audience Sentiments in Movie Tweets

Deep Learning Models for Understanding Audience Sentiments i...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： S. Ram Prasath J.K. Jeevitha S Margret R. Santhana Krishnan J. Relin Francis Raj Ezhil E Nithila Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Information Technology PSNA College of Engineering and Technology Dindigul India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

this study explores the application of deep learning models for analyzing audience sentiments in movie tweets, aiming to provide a comprehensive understanding of audience reception. Leveraging Twitter data collected using Tweepy and supplemented with web-scraped content via BeautifulSoup, the study employs advanced Natural language processing (NLP) techniques. Text preprocessing using SpaCy ensures standardized input for subsequent analysis. Sentiment analysis utilizes RoBERTa, an advanced deep learning transformer model known for its robust performance in NLP tasks. RoBERTa effectively classifies tweets into positive, negative, or neutral sentiments, leveraging its contextual understanding to interpret nuanced language expressions prevalent in social media discourse. Simultaneously, RoBERTa is employed for emotion detection, identifying emotional states such as joy, anger, sadness, and more, crucial for gauging audience emotional responses to various aspects of movies. this dual-analysis framework provides detailed insights into audience reactions, facilitating a nuanced evaluation of movie reception across diverse demographic segments. Additionally, the study explores aspect-based sentiment analysis to delve deeper into specific movie attributes like plot, acting, and special effects. Visualizations using Plotly enhance the interpretability of sentiment and emotion distributions over time, supporting informed decision-making in the entertainment industry.

关键词： Deep learning Sentiment analysis Analytical models Accuracy Social networking (online) Reviews Decision making Entertainment industry Motion pictures Data models

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Adaptive Framework for Comprehensive Quality Assessment in Unstructured Big Data

Adaptive Framework for Comprehensive Quality Assessment in U...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Gopal Kaliyaperumal Anitha Gatla Sorabh Lakhanpal K. Jyothsna Reddy Rusul Najm K. Butchi Raju Department of Mechanical Engineering New Horizon College of Engineering Bangalore India Department of Information Technology Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of CSE GRIET Bachupally Hyderabad Telangana India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

the current growth and evolution have led to the emergence of structures such as unstructured big data in different fields creating a need and demand for effective quality assessment models specifically designed for big data. this work presents a new Big Data Quality Assessment Model (BDQAM) that is tailored for use with unstructured data since they are highly diverse, a have no prior planned structure, and semantics are complex. the BDQAM, therefore, uses cutting edge machine learning algorithms and natural language processing tools to perform a real time assessment and validation on big data with the view of determining the quality of the data accumulated. the quality assessment is based on key quality dimensions and the assessment of accuracy, completeness, consistency and timeliness; the model is designed to learn from experience and improve the results. the effectiveness of the proposed model is affirmed by testing on a variety of large-scale unstructured datasets across different domains together with substantial enhancements in precision and recall measurements in contrast with the conventional quality assessment methodologies. the study results enhance practice in big data analysis by offering a solution that can be applied and is effective in checking the validity of Big Data inside decision-making processes.

关键词： Analytical models Solid modeling Accuracy Machine learning algorithms Decision making Big Data Data models Real-time systems Natural language processing Quality assessment

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Classifying Sentiments on Social Media Texts: A GPT-4 Preliminary Study 23

Classifying Sentiments on Social Media Texts: A GPT-4 Prelim...

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Proceedings of the 2023 7th international conference on Natural language processing and information Retrieval

作者： Lany Laguna Maceda Jennifer Laraya Llovido Miles Biago Artiaga Mideth Balawiswis Abisado Computer Science and Information Technology Department Bicol University Philippines College of Computing and Information Technologies National University Philippines

ISBN: (纸本)9798400709227

In today's digital age, social media has become a hub for people to express their thoughts and feelings. Sentiment classification discerns public opinions and trends to understand their sentiments towards a certain topic. Often, achieving accurate sentiment classifications in large datasets necessitate the use of human-annotated training data which can be costly and time-consuming. Large language Models (LLMs) like the Generative Pre-trained models by OpenAI have surged in popularity due to its capabilities in understanding the given tasks. In this preliminary study, we report the performance of the latest OpenAI GPT-4 using zero- and one-shot learning approaches on classifying sentiments when fed with social media dataset. Notably, the latter approach written in English which mimics the instructions designed for human annotators, achieved a substantial agreement (k = 0.77) with human annotations, displaying high accuracy, precision, and recall accordingly even without explicit training data. Meanwhile, the fine-tuned mBERT resulted to lower evaluation scores than the GPT-4. Our findings provide foundational insights into the strengths and limitations of GPT-4 for sentiment classification in a social media dataset, setting the groundwork for broad future research in this field.

关键词： GPT-4 LLM Prompting Sentiment Annotation Social Media Data

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