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Attention-Based Deep Learning Algorithm in natural language processing for Optical Character Recognition

Attention-Based Deep Learning Algorithm in Natural Language ...

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2023 international conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Prem Kumar, M. Logesh Babu, R. Sunil, G. Sampathkumar, J. Chakraborty, Subhra Global Technical Marketing Element 14 Bangalore India Kpr Institute of Engineering & Technology Department of Information Technology Coimbatore India School of Computer Science & Artificial Intelligence Sr University Warangal India Mahendra College of Engineering Department of Ece Salem India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350313413

Optical Character Recognition (OCR) is commonly referred as text recognition which poses a substantial issue in the computer vision tasks. Conventional optical character recognition systems frequently suffer in handwritten document recognition. To solve this, Deep Learning (DL) models have emerged as a powerful and advanced solution for character recognition. The present research offers a unique CNN-RNN model with an Attention Mechanism (CNN-RNN-AM) for English image character recognition. The process comprised many important phases, beginning with image collection from a user-defined dataset, then image pre-processesing includes grayscale conversion and noise reduction. For effective character recognition, the proposed approach integrates the segmentation process at multiple levels, including line segmentation, word segmentation, and character segmentation. Finally, the CNN-RNN with an attention mechanism is deployed for character recognition. The experimental findings demonstrated the remarkable efficacy of the suggested CNN-RNN-AM model. It outperformed other compared models by attaining an excellent character recognition accuracy of 99.89%. © 2023 ieee.

关键词： Attention Mechanism Computer Vision Deep Learning Grayscale Conversion and Optical Character Recognition

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Zero-Shot Scene Graph Generation with knowledge Graph Completion

Zero-Shot Scene Graph Generation with Knowledge Graph Comple...

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2022 ieee international conference on Multimedia and Expo, ICME 2022

作者： Yu, Xiang Chen, Ruoxin Li, Jie Sun, Jiawei Yuan, Shijing Ji, Huxiao Lu, Xinyu Wu, Chentao Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Limited by the incomprehensive training samples, existing scene graph generation (SGG) methods perform poorly on predicting zero-shot (i.e., unseen) subject-predicate-object triples. To address this problem, we propose a general SGG framework to improve their zero-shot performance. The main idea of our method is to generate the information of zero-shot triples before the training of the predicate classifier and thus make the original zero-shot triples non-zero-shot. Specifically, the missing information of zero-shot triples is generated by our proposed knowledge graph completion strategy and then integrated with visual features of images. Therefore, the predicate classification of zero-shot triples is no longer just regarded as a single visual classification task but also transformed into a prediction task of missing links in a knowledge graph. The experiments on the dataset Visual Genome demonstrate that our proposed method outperforms the state-of-the-art methods in popular zero-shot metrics (i.e., zR@N, ng-zR@N) for all popular SGG tasks. © 2022 ieee.

关键词： Classification (of information)

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FormalStyler: GPT based Model for Formal Style Transfer based on Formality and Meaning Preservation 13

FormalStyler: GPT based Model for Formal Style Transfer base...

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13th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management (IC3K) / 13th international conference on knowledge Discovery and Information Retrieval (KDIR)

作者： de Rivero, Mariano Tirado, Cristhiam Ugarte, Willy Univ Peruana Ciencias Aplicadas UPC Lima Peru

ISBN: (纸本)9789897585333

Style transfer is a natural language processing generation task, it consists of substituting one given writing style for another one. In this work, we seek to perform informal-to-formal style transfers in the English language. This process is shown in our web interface where the user input a informal message by text or voice. This project's target audience are students and professionals in the need to improve the quality of their work by formalizing their texts. A style transfer is considered successful when the original semantic meaning of the message is preserved after the independent style has been replaced. This task is hindered by the scarcity of training and evaluation datasets alongside the lack of metrics. To accomplish this task we opted to utilize OpenAI's GPT-2 Transformer-based pre-trained model. To adapt the GPT-2 to our research, we fine-tuned the model with a parallel corpus containing informal text entries paired with the equivalent formal ones. We evaluate the fine-tuned model results with two specific metrics, formality and meaning preservation. To further fine-tune the model we integrate a human-based feedback system where the user selects the best formal sentence out of the ones generated by the model. The resulting evaluations of our solution exhibit similar to improved scores in formality and meaning preservation to state-of-the-art approaches.

关键词： natural language processing Style Transfer GPT-2 Formalization Meaning Preservation Transformer

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Mobile Applications Powered by AI for Early Detection of Chronic Disease 2

Mobile Applications Powered by AI for Early Detection of Chr...

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2nd ieee international conference on Emerging Trends in engineering and Medical Sciences, ICETEMS 2024

作者： Apetorgbor, Mawuli Narad, Supriya Raymond, David Amanda Fortune, Nyamador Abba Konto, Abdulrahman Doe, Martin Datta Meghe Institute of Higher Education and Research Faculty of Science and Technology Wardha India Datta Meghe Institute of Higher Education and Research Department of Computer Science and Medical Enginering Wardha India Datta Meghe Institute of Higher Education and Research Department of Physician Assistant Wardha India Datta Meghe Institute of Higher Education and Research Department of Medical Laboratory Technology Wardha India Valley View University Department of Computer Science and Engineering Accra Ghana

ISBN: (纸本)9798331529352

Artificial intelligence (AI)-driven smartphone applications, utilizing state-of-the-art technology like natural language processing, deep learning, and machine learning, are transforming the early diagnosis and treatment of chronic illnesses. These applications enable continuous health monitoring and extensive insights into individual health states through the use of modern sensors, real-time data analytics, and the Internet of Things (IoT). This analysis examines the state of AI technologies in mobile health applications today, emphasizing how useful they are for cost-cutting, personalized health monitoring, and early disease diagnosis. The advantages of better diagnosis accuracy, prompt interventions, and increased patient involvement as well as the drawbacks of data privacy, legal compliance, algorithm bias, and the requirement for ongoing updates are explored. Significant developments in data interoperability, sensor technology, and AI algorithms are anticipated in the future, which will expand the potential of these applications. Computer-based intelligence-controlled portable well-being applications can alter medical care by handling recent concerns through cooperation. They could expand openness, proficiency, and adequacy of early persistent disease distinguishing proof, which would ultimately work on tolerant results and personal satisfaction. © 2024 ieee.

关键词： AI-driven mobile Apps Customized Health Tracking Using Sensor Technology Identification of Chronic Diseases Innovation in Health care Prompt Diagnosis Machine Learning

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Enhancing the Automatic Identification of Common Math Misconceptions Using natural language processing 24th

Enhancing the Automatic Identification of Common Math Miscon...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Gorgun, Guher Botelho, Anthony F. Univ Alberta Edmonton AB T6G 2G5 Canada Univ Florida Gainesville FL 32611 USA

ISBN: (纸本)9783031363351;9783031363368

In order to facilitate student learning, it is important to identify and remediate misconceptions and incomplete knowledge pertaining to the assigned material. In the domain of mathematics, prior research with computer-based learning systems has utilized the commonality of incorrect answers to problems as a way of identifying potential misconceptions among students. Much of this research, however, has been limited to the use of close-ended questions, such as multiple-choice and fill-in-the-blank problems. In this study, we explore the potential usage of natural language processing and clustering methods to examine potential misconceptions across student answers to both close- and open-ended problems. We find that our proposed methods show promise for distinguishing misconception from non-conception, but may need further development to improve the interpretability of specific misunderstandings exhibited through student explanations.

关键词： misconceptions sentence-BERT intelligent tutoring system natural language processing

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CAVI: A Contrastive Image Captioning Framework with the Contributions of All Visual Information

CAVI: A Contrastive Image Captioning Framework with the Cont...

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international conference on Signal processing Proceedings (ICSP)

作者： Ziwei Shang Gaoyun An Shan Cao Institute of Information Science Beijing Jiaotong University Beijing China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China College of Information Engineering Capital Normal University Beijing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Image captioning is a multimodal task combining computer vision (CV) and natural language processing (NLP). Contrastive language image pre-training has made significant progress by providing effective multimodal representation. In this paper, we propose a contrastive learning-based perceptual image captioning model that utilizes all visual *** make improvements in visual encoder, mainly reflected in first storing the visual information encoded by each layer of encoder, and obtaining aligned visual input by averaging to avoid the loss of key visual *** results show that CAVI improves the ability to describe the relationship between image objects and enhances the quality of generated descriptions. It performs well on the relevant indicators of the Flickr dataset, and also demonstrates certain cross-domain description capabilities on the COCO dataset as a validation set.

关键词： Visualization Computer vision Image databases Computational modeling Contrastive learning Signal processing natural language processing Decoding Web sites Multimedia communication

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Comparative Analysis of Machine Learning Models for Detecting Mobile Messaging Spam In Swahili SMS 1

Comparative Analysis of Machine Learning Models for Detectin...

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1st international conference on the Advancements of Artificial Intelligence in African Context, AAIAC 2023

作者： Kalolo, Christopher Mbelwa, Jimmy Eastern Africa Statistical Training Centre Department of Official Statistics Dar es Salaam Tanzania United Republic of University of Dar Es Salaam Department of Computer Science and Engineering Dar es Salaam Tanzania United Republic of

ISBN: (纸本)9798350330137

The prevalence of spam messages is a worldwide issue. Unsolicited text messages in Swahili via SMS have emerged as a significant concern for mobile phone users. This surge can be attributed to the increasing number of mobile phone users who favor SMS as their primary means of communication. Various techniques and strategies have been developed to counteract unwanted SMS messages in various languages. Nonetheless, languages with limited research and a lack of publicly available Swahili SMS datasets are more susceptible to spam texts. This research conducted a comparative examination of machine learning models for addressing spam in Swahili mobile messages. We employed six machine learning models and assessed their performance using various classification metrics. The comprehensive assessment favored the Support Vector Classifier (SVC) model as the top performer. The SVC model achieved an accuracy rate of 96.19%, a precision of 97.39%, a recall of 94.92%, an F-Measure of 96.14%, and an AUC-ROC score of 0.9619. Following closely was the Logistic Regression model, boasting an accuracy rate of 95.35%, precision of 96.93%, recall of 93.64%, an F-Measure of 95.26%, and an AUC-ROC score of 0.9535. The overall evaluation indicated that all machine learning models displayed promising performance. Results demonstrated that these models exhibit strong discriminatory capabilities between positive and negative classes, as evidenced by their higher AUC-ROC scores. In conclusion, we recommend further research in this domain and in other natural language processing tasks specific to the Swahili language. © 2023 ieee.

关键词： Learning algorithms

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A Review of Alzheimer's Disease Detection from Spontaneous Speech and Text 5

A Review of Alzheimer's Disease Detection from Spontaneous S...

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5th international conference on Control, Communication and Computing, ICCC 2023

作者： Vrindha, M.K. Geethu, V. Anurenjan, P.R. Deepak, S. Sreeni, K.G. College of Engineering Computer Vision Lab Kerala Trivandrum India Rajiv Gandhi Institute of Technology Kerala Kottayam India APJ Abdul Kalam Technological University Kerala Trivandrum India

ISBN: (纸本)9798350334128

In the past decade, there has been a surge in research examining the use of voice and speech analysis as a means of detecting neurodegenerative diseases such as Alzheimer's. Many studies have shown that certain acoustic features are very useful in differentiating memory changes due to normal aging from Alzheimer's disease, and speech analysis is found to be an economical way of identifying dementia. This review aims to analyze the various algorithms used in communication-based identification and grouping of Alzheimer's disease. A literature survey was conducted using databases such as Web of Science, Google Scholar, and Science Direct, and articles published from January 2020 to the present were included based on keywords such as 'speech,' "Alzheimer's detection,"and 'natural language processing.' The ADReSS, Pitt corpus, and Carolinas Conversation Collection (CCC) datasets are commonly used for the analysis of dementia from speech, and this review focuses on the various acoustic and linguistic feature engineering-based classification models drawn from 15 *** on this study's findings, it appears that a more accurate model for classifying Alzheimer's disease can be developed by considering both linguistic and acoustic data. The review suggests that speech signals can be a useful tool for detecting dementia and can be a reliable biomarker for efficiently identifying Alzheimer's Dementia. © 2023 ieee.

关键词： Neurodegenerative diseases

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Prompt as a knowledge Probe for Chinese Spelling Check 15th

Prompt as a Knowledge Probe for Chinese Spelling Check

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15th international conference on knowledge Science, engineering, and Management (KSEM)

作者： Peng, Kun Sun, Nannan Cao, Jiahao Liu, Rui Ren, Jiaqian Jiang, Lei Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9783031109898;9783031109881

Chinese Spelling Check (CSC) is a challenging task to detect and correct wrong characters in Chinese sentences. Since most Chinese spelling mistakes are caused by visual or pronunciation similarities of characters, recent researches tend to utilize external phonological and morphological resources for this task. However, their works rely heavily on hand-constructed confusion sets and multimodal data, causing high labor costs. To this end, we propose an end-to-end generative model called PromptCSC. First, we notice that the misspelling of characters causes unnatural semantic incoherence in sentences. By using the prompt template as a knowledge probe, PromptCSC detects and outputs the error probability of each character in the sentence. The error locations are then corrected using BERT's soft mask mechanism. Experimental results on the SIGHAN benchmarks show that our approach achieves excellent performance without external resources.

关键词： Chinese Spelling Check Prompt-tuning natural language processing Pre-training model BERT

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Efficiency & Innovation in Recruitment usingAI & Android

Efficiency & Innovation in Recruitment usingAI & Android

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

作者： Kanika Chalana Ashwin Ravi Gautam Anand Raj J.S. Daivik H.R. Amrutha Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bengaluru

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

The goal of screening resumes is to determine the top applicants for a position and to inform users of their resume score and areas for improvement. The literature on existing approaches has been analyzed, and it has been discovered that the traditional systems like manual screening may result in false assumptions and the wasting of human potential, but they lackrobustness Regarding processing, accuracy and efficiency. To acquire accurate results, software must utilized to process natural language and machine learning techniques to match and rate the candidates in real-time by ranking their resumes. The input would be the resumes and output would be a ranked candidate’s resumes on the admin side and recommendations for the user side. Instantaneous real-time output results are acquired by employing Object character recognition. In The system that is being suggested the authors used OCR model and android principles along with python scripts. This system has the many benefits including security, interpretability, high accuracy, lightweight model, and quick processing.

关键词： Technological innovation Accuracy Resumes Optical character recognition natural languages Manuals Real-time systems

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