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A Comparative Study of LSTM, Bi-LSTM, and BERT for Automated Essay Scoring

A Comparative Study of LSTM, Bi-LSTM, and BERT for Automated...

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international conference on advanced information Technologies (ICAIT)

作者： Khin Su Su Han May thu Myint University of Technology (Yatanarpon Cyber City) Pyin Oo Lwin Myanmar University of Information Technology Yangon Myanmar

ISBN: (数字)9798331510046

ISBN: (纸本)9798331510053

the rapid progress in Artificial Intelligence (AI) and natural language processing (NLP) has created new possibilities for educational technology, particularly in automated essay scoring (AES). this research explores the potential of Artificial Intelligence (AI) in revolutionizing essay scoring within educational technology. Specifically, it focuses on comparing the effectiveness of three distinct approaches to automated essay scoring (AES): training a Long Short-Term Memory (LSTM) model, Bidirectional Long Short-Term Memory (Bi-LSTM) from scratch, and fine-tuning a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model. Both models are trained and finetuned for each approach using the ASAP dataset from Kaggle’s Learning Agency Lab - Automated Essay Scoring 2.0 competition. their performance is evaluated based on metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and Accuracy. To facilitate practical application, a Python web application built with Streamlit enables users to submit essays, choose a scoring model (LSTM, Bi-LSTM or BERT), and receive feedback based on the selected model’s prediction. this study contributes valuable insights into the strengths and weaknesses of each approach, furthering our understanding of efficient model selection for AES tasks.

关键词： Training Measurement Bidirectional control Educational technology Predictive models Transformers Encoding Natural language processing Artificial intelligence Long short term memory

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Applying ontological modeling on quranic "nature" domain

Applying ontological modeling on quranic "nature" domain

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international conference on information and Communication Systems (ICICS)

作者： A. B. M. Shamsuzzaman Sadi Towfique Anam Mohamed Abdirazak Abdillahi Hasan Adnan Sazid Zaman Khan Mohamed Mahmudur Rahman Ghassan Samara Department of Computer Science and Engineering International Islamic University Chittagong Bangladesh Department of Science and Technology Universiti Sains Islam Malaysia Nilai Malaysia Department of Internet Technology Zarqa University Jordan

ISBN: (纸本)9781467386159

the holy Quran is the holy book of the Muslims. It contains information about many domains. Often people search for particular concepts of holy Quran based on the relations among concepts. An ontological modeling of holy Quran can be useful in such a scenario. In this paper, we have modeled nature related concepts of holy Quran using OWL (web Ontology language) / RDF (Resource Description Framework). Our methodology involves identifying nature related concepts mentioned in holy Quran and identifying relations among those concepts. these concepts and relations are represented as classes/instances and properties of an OWL ontology. Later, in the result section it is shown that, using the Ontological model, SPARQL queries can retrieve verses and concepts of interest. thus, this modeling helps semantic search and query on the holy Quran. In this work, we have used English translation of the holy Quran by Sahih international, Protege OWL Editor and for querying we have used SPARQL.

关键词： Ontologies Semantics OWL Search engines Resource description framework Data mining

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Deriving Pipeline for Emergency Services Using Natural language processing Techniques 11th

Deriving Pipeline for Emergency Services Using Natural Langu...

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11th international conference on Recent Trends in Computing, ICRTC 2022

作者： Anand, Akshat Rajeswari, D. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India

ISBN: (纸本)9789811988240

Communications have a crucial role in times of crisis, particularly in times of emergency. Whenever a region is affected by any disaster, social media sites such as Twitter, etc., are a great way to get information out to those who need it the most. Our paper suggests that in times of crisis, when people are worried about finding the right emergency contacts or are experiencing a transformation in their psychological response, they should manually enter their experience into an app to obtain contact information for the nearest SOS. Alternatively, the proposed application suggests scraping hashtags from around the world with web scraping scripts to retrieve geo-referenced tweets in real time. In order to gather accurate data, Twitter's API or some third-party application can be used in the development of this software. When there is a major crisis, social media user behavior plays a vital role, owing to its efficiency and high reliability with swift data dissemination. Anxiety over finding the right emergency team contact may cause people to lose their cool and become unclear about which authority they should call when an emergency occurs. Eventually, our application bridges the gap between clients in need of help and the approved agency that can really provide that support in different circumstances. In the case of, for example, a lost child or a flooded area, our application helps you get in touch with an organization that can assist you in dealing with the situation. We have used CNN-1-D + LSTM, CNN-1-D + Bi-LSTMs, and CNN-1-D + LSTM + Glove-100-D to get maximum accuracy for the classification model. It is possible to use the prototype solution in a novel way to organize immediate assistance with authorities in comprehensive administration, while integrating Twitter or other social platforms into the application can make it a feasible platform for preventive intervention and catastrophe management. © 2023, the Author(s), under exclusive license to Springer Nature

关键词： Emergency services

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Cloud Computing and Robotics for Disaster Management

Cloud Computing and Robotics for Disaster Management

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international conference on Intelligent Systems, Modelling and Simulation (ISMS)

作者： Nitesh Jangid Bindu Sharma Information Technology Department Green Research IT Solutions Pvt. Ltd. Jaipur Rajasthan India Amity School of Fashion Technology Amity University Jaipur Jaipur Rajasthan India

ISBN: (纸本)9781509006663

Nowadays, IT community is experiencing great shift in computing and information storage infrastructures by using powerful, flexible and reliable alternative of cloud computing. the power of cloud computing may also be realized for mankind if some dedicated disaster management clouds will be developed at various countries cooperating each other on some common standards. the experimentation and deployment of cloud computing by governments of various countries for mankind may be the justified use of IT at social level. It is possible to realize a real-time disaster management cloud where applications in cloud will respond within a specified time frame. If a Real-Time Cloud (RTC) is available then for intelligent machines like robots the complex processing may be done on RTC via request and response model. the complex processing is more desirable as level of intelligence increases in robots towards humans even more. therefore, it may be possible to manage disaster sites more efficiently with more intelligent cloud robots without great lose of human lives waiting for various assistance at disaster site. Real-time garbage collector, real-time specification for Java, multicore CPU architecture with network-on-chip, parallel algorithms, distributed algorithms, high performance database systems, high performance web servers and gigabit networking can be used to develop real-time applications in cloud.

关键词： Cloud computing Real-time systems Robots Java Multicore processing Hardware Operating systems

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Simulation research and design of user behavior prediction for smart home Internet of things 23

Simulation research and design of user behavior prediction f...

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Proceedings of the 2023 7th international conference on Electronic information technology and Computer Engineering

作者： Jinmei Shi Qingcheng Zhao Ailing Zhou Peng Ye College of Information Engineering Hainan Vocational University of Science and Technology China

ISBN: (纸本)9798400708305

Scientific and technological innovation to build a new development pattern, promote the development of high-quality technology, in today's rapid change, people's demand for life is getting higher and *** this stage, the technology adopted by different smart home enterprises is similar, and the products of different brands are incompatible with each other, the cost is high, and the control method is single, which is far from meeting the growing needs of *** topic will focus on the development bottleneck of smart home, using STM32 microcontroller as the main controller, integrating various sensors to collect terminal data, processing data based on speech recognition to realize user emotion recognition and behavior prediction, and then transmitting data through wireless communication technology. Finally, HTML5+JavaScript language for the development basis through the web side *** system synthetically uses a variety of sensors and combines intelligent algorithms to predict behavior to meet the real needs of users, which can realize further research on smart home systems and provide technical reference for the development of this field.

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Research on Educational Knowledge Base Question Answering System Based on Large language Model

Research on Educational Knowledge Base Question Answering Sy...

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Computer information Science and Application technology (CISAT), international conference on

作者： Yonggang Gong Hongyu Liu Xiaoqin Lian Guochun Ma Beijing Technology and Business University Beijing China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

In recent years, methods based on pre-trained large models have received extensive attention in the field of natural language processing. the large language model has the potential to reduce the development cost of question answering systems in the education field and improve the accuracy of question answering system. In this paper, based on the curriculum standards of compulsory education, an educational knowledge base is constructed by using the method of label alignment optimization, and a professional large language model question answering system is constructed based on the large language model to meet the requirements of the new curriculum standards, so as to generate a high-availability teaching scheme. Secondly, a prompt word method based on educational keyword template is proposed, aiming at improving the quality of question answering generation of large language model in educational question answering system. the experimental results show that this system is superior to the universal large language model in both objective and subjective evaluation, and has higher usability in the field of education and more accuracy in generating answers.

关键词： Accuracy Annotations Large language models Knowledge based systems Education Question answering (information retrieval) Usability Standards Optimization Tuning

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Utilization Model using Support Vector Machine Classification for Disaster-Related Post on Twitter

Utilization Model using Support Vector Machine Classificatio...

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the 10th international conference on Arts, Social Sciences, Humanities and Interdisciplinary Studies (ASSHIS-17);the 11th international conference on Law, Education, Business and Management (LEBM-17);the 6th international conference on Advances in Science, Engineering and technology (ICASET-17);the 9th international conference on Chemical, Agricultural, Biological and Environmental Sciences (CABES-17);the 7th international conference on Civil, Architecture, Ecology and Waste Management (CAEWM-17)

作者： Randy Joy Ventayen Pangasinan State University

Philippine is known as part of the ring of fire, it is one of the disaster-prone countries which was hit by typhoon Haima(with a local name of Lawin), and followed by Sakira(with a local name of Karen) which was hit the country last October 2016, the two typhoons were named as one of the strongest typhoons that hit the country. In today's generation, Twitter served as a communication outlet and a source of information. On some numbers of tweets in social media, there is local language posted by the local users, and this study will bridge the gap since for those speaking another major language in the Philippines. the study will be sought to answer on how to download twitter data from a specific disaster duration in the region, how to extract and identify multilingual disaster-related tweets and finally how to classify disaster and non-disaster tweets in the local language. the study of classification and extraction of disaster and emergency-related tweets is important is interesting study because the life of a person which speaks a very rare dialect is important as the same as the person speaking a major language. the model presented in this study is useful as a tool to extract tweets, filter, and classify that could utilize for a faster response that could save a life.

关键词： Natural language processing Social Media Twitter

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Natural language processing and Deep Learning Techniques to Improve Sentiment Analysis in Social Media Texts

Natural Language Processing and Deep Learning Techniques to ...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Sreenivas Mekala M. Bhuvana Dev Brat Gupta Vinod Bhatt Pankaj Kunekar Geetha Manoharan Information Technology Sreenidhi Institute of Science and Technology Hyderabad Telangana India College of Engineering SUM Institute of Science & Technology Kottankulathur Tamil Nadu School of Advanced Sciences and Languages VIT Bhopal University Madhya Pradesh India Department of Information Technology Vishwakarma Institute of Technology Pune Maharashtra India School of Business SR University Warangal Telanagana

Sentiment analysis (SA) is a mechanized strategy for finding and understanding the feelings depicted in text. Over the most recent decade, SA has altogether expanded in ubiquity in the Natural language processing (NLP) people group. Because of the inescapable utilization of social media and web stages, SA is presently urgent for firms to get client info and shape their promoting approach. the motivation behind this study is to utilize a deep learning-based way to deal with natural language process to give an exhaustive investigation of the opinion of sentiment of social media content. the technique then builds a comparable model that may be applied to replicate the relevant social process. In instance, by dynamically choosing the most significant phrase in the current state depending on the information presented, the network can effectively recognize the significant material that is always changing. this prompts the semantic comprehension of the total sentence toward the finish of each phase of the interaction. Besides, an absence of context oriented data will prompt an incorrect and muddled semantic portrayal of natural language on the grounds that relevant data is fundamental for that portrayal. In this paper, we take a gander at the unimodal and multimodal social network sentiment analysis algorithms and foster two models for text sentiment investigation and picture-text multimodal sentiment examination.

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web Based Signature Authenticity Application 23

Web Based Signature Authenticity Application

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

作者： Ryan Tabayoyong Rillera Leonard Alejandro Information Technology and Information System Department Adamson University Philippines

ISBN: (纸本)9798400709227

Our signatures are very important to our daily lives, as they anchor the agreement to a legally binding, enforceable, and actionable reality. the old-style purpose of a signature is to permanently affix to a document a person's uniquely personal, undeniable self-identification as physical evidence of that person's personal witness and certification of the content of all, or a specified part, of the document. the purpose of the proposed capstone project is to determine the authenticity of a signature in an important document, namely, certificates, checks, legal documents, letters, and receipts. Automatically identifies a signature without human intervention. the overall objective of the proposed capstone project is to have immediate and accurate results of an investigation whether the signature is legitimate or counterfeit. the conclusion of the study shows that image recognition is the core development of the project, with the use of Artificial Intelligence, feeding the database with images will have a higher accuracy rate in authenticating signatures.

关键词： Bio-metrics Front-end Mobile Browser Stroke Analysis

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Transformer-Based Models for Commodity Trading Price Forecasting

Transformer-Based Models for Commodity Trading Price Forecas...

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Data Science and information technology (DSIT), international conference on

作者： Peerada Kampotha Hua Wang Chao Li International Business School Zhejiang University China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

Transformer-based models have gained significant traction across various fields such as natural language processing (NLP), large language models (LLM), smart transportation, and finance due to their ability to capture long-term dependencies and sequence patterns. However, their application in forecasting stock or commodity trading returns remains underexplored. In this study, we aim to compare the performance of three transformer-based models—Informer, Autoformer, and Transformer with Positional Encoder unit —alongside an LSTM for forecasting commodity returns. Specifically, the two datasets used will consist of historical commodity prices, including six types of metals. these models, known for their capacity to handle long sequence data, have shown promise in financial forecasting, particularly. Still, their potential in commodity trading price forecasting has been less studied. By evaluating these models based on historical metal prices, this research seeks to determine the most accurate model for predicting commodity returns, providing valuable insights for the commodity trading and financial sectors.

关键词： Accuracy Large language models Metals Predictive models Transformers Natural language processing Data models Smart transportation Forecasting Long short term memory

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