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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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An Interactive System leveraging Automatic Speech Recognition and machine Translation for learning Hindi as a Second Language 3

An Interactive System leveraging Automatic Speech Recognitio...

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3rd international conference for Emerging Technology, INCET 2022

作者： Rohil, Mukesh Kumar Saini, Shubham Rohil, Rohan Kumar Birla Institute of Technology and Science Pilani Department of Computer Science and Information Systems Pilani India Birla Institute of Technology and Science Pilani Department of Mathematics Pilani India

ISBN: (纸本)9781665494991

When English speakers are in the early stages of learning Hindi, formulating sentences in Hindi is often attempted by a verbatim translation of English words to corresponding Hindi words. Due to this reason, they are unable to learn Hindi sentences correctly. We have tried to overcome this problem by use of technology for second language learners. The use of Automatic Speech Recognition, and machine Translation for second language learning, here learning Hindi by English speaker, has been illustrated by taking English speech as input and translating the given English sentences and words into Hindi and then displaying its equivalent construct in Devanagari script. The interactive system under study displays and speaks the same. It has been observed that a second language can be learnt faster by frequently listening to the vocabulary and sentences of the language. Thus the system furnishes the functionality of speaking the sentence in Hindi once it is represented in Devanagari script. The English sentences and words from the grammar tool books are given as input to the system for experimentation. We have observed that the critical problem encountered while doing so is the translation of English to Hindi. Another problem encountered at times is insertion error for letters (only surfaced). The system cannot translate sentences represented using continuous tense and perfect continuous tense correctly. The overall accuracy of the system, otherwise, is approximately 67% which can help the second language learners in the beginning. © 2022 IEEE.

关键词： Speech recognition

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Construction of a Japanese-Chinese Bilingual Corpus for learning Japanese Sentence Patterns through Multi-Level Annotation 3

Construction of a Japanese-Chinese Bilingual Corpus for Lear...

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3rd international conference on Pattern Recognition and machine learning, PRML 2022

作者： Liu, Jun Zhuang, Luxuan Guangxi University School of Foreign Languages and Literatures Nanning China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

This work introduces the construction of a Japanese-Chinese bilingual corpus, aiming to help Chinese-speaking learners of Japanese as a second language (JSL) with their study of Japanese sentence patterns in Japanese grammar. For constructing this Japanese-Chinese bilingual corpus, methods of automatic identification of the Japanese sentence patterns were proposed by annotating a dataset on multiple levels for training a model based on Conditional Random Fields (CRF) machine learning algorithm. The multi-level annotation performed is Part-of-speech (POS) tagging, which includes the detailed information, such as surface form, dictionary form, pronunciation, difficulty level and etc. The well-trained model is applied to automatic identification and suggestion of the Japanese sentence patterns with different difficulty levels according to Japanese Language Proficiency Test (JLPT). We evaluated the model on a test dataset in order to show the effectiveness of our proposed methods. We also show how we used this model to construct a Japanese-Chinese bilingual corpus, and manually corrected the example sentences where the Japanese sentence patterns were identified incorrectly or not identified. This model can be effectively used for the Japanese language processing framework, which allows for a fast and easy application for the problem of Japanese grammar extraction. Moreover, the constructed Japanese-Chinese bilingual corpus can be applied as a useful computer-assisted language learning (CALL) tool for learning Japanese sentence patterns. © 2022 IEEE.

关键词： learning algorithms

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Digit Recognition by the Implementation of Supervised learning Using a Convolutional Neural Network 3

Digit Recognition by the Implementation of Supervised Learni...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Paliwal, Mrinal Kumar Chawla, Sunil Soni, Punit Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350342338

The recognition of handwritten numbers is a significant challenge in the field of computer vision, as it finds applications in various domains including banking, medical diagnostics, and handwriting recognition. In recent years, there has been a notable enhancement in our technological capabilities due to the progress made in internet accessibility and technology. Deep learning algorithms have emerged as valuable computational tools for addressing several challenges, including the task of digit recognition. The utilization of convolutional neural networks in the task of digit recognition is driven by their ability to acquire hierarchical representations from picture data. This scholarly article provides a comprehensive examination of the application of Convolutional Neural Networks (CNNs) in the field of machine learning for the purpose of digit recognition. This research examines the architectural components of Convolutional Neural Networks (CNNs), the training procedures employed, and the various methodologies employed for the purpose of digit recognition. The paper also provides an overview of the different datasets used for training and testing CNNs for digit recognition and a discussion of the results obtained from various studies. © 2023 IEEE.

关键词： Convolutional Neural Network Digit Recognition MNIST database Neural Network Supervised learning

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The 3rd international Workshop on Mining and learning in the Legal Domain 23

The 3rd International Workshop on Mining and Learning in the...

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32nd ACM international conference on Information and Knowledge Management (CIKM)

作者： Makrehchi, Masoud Zhang, Dell Petrova, Alina Armour, John Thomson Reuters Labs Toronto ON Canada Thomson Reuters Labs London England Univ Oxford Oxford England

ISBN: (纸本)9798400701245

The increasing accessibility of legal corpora and databases create opportunities to develop data-driven techniques and advanced tools that can facilitate a variety of tasks in the legal domain, such as legal search and research, legal document review and summary, legal contract drafting, and legal outcome prediction. Compared with other application domains, the legal domain is characterized by the huge scale of natural language text data, the high complexity of specialist knowledge, and the critical importance of ethical considerations. The MLLD workshop aims to bring together researchers and practitioners to share the latest research findings and innovative approaches in employing data mining, machine learning, information retrieval, and knowledge management techniques to transform the legal sector. Building upon the previous successes, the third edition of the MLLD workshop will emphasize the exploration of new research opportunities brought about by recent rapid advances in Large Language Models and Generative AI. We encourage submissions that intersect computer science and law, from both academia and industry, embodying the interdisciplinary spirit of CIKM.

关键词： legal data mining legal information retrieval legal natural language processing legal knowledge management large language models

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Enhanced Extreme learning machine Algorithm with Deterministic Weight Modification for Investment Decision on Indian Stocks 3

Enhanced Extreme Learning Machine Algorithm with Determinist...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Kalaiselvi, K. David, Vasantha Kalyani Avinashilingam University for Women Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Predicting stock market performance has long been a popular research topic because of its high potential for financial gain. One important task of stock market analysis is being able to reasonably predict future stock price. Recent years have seen rapid advancement in disciplines like machine learning, which has provided fresh perspectives on this task. This paper presents an enhanced extreme learning machine (ELM) for predicting the stock price. The enhanced ELM uses a new weight adjustment scheme such as deterministic weight medication (DWM) for enhances prediction accuracy and provide faster convergence. A new DWM based ELM (DELM) prediction algorithm is to improve the global convergence rate by increasing the gradient values of error surface. The experimental results are conducted on financial datasets thoroughly trained and tested. The experimental findings revealed that the suggested DELM prediction model produces better prediction performance when compared with other well-known prediction algorithms. © 2022 IEEE.

关键词： Neural networks

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3rd Biennial international conference on Future learning Aspects of Mechanical Engineering, FLAME 2022

3rd Biennial International Conference on Future Learning Asp...

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3rd Biennial international conference on Future learning Aspects of Mechanical Engineering, FLAME 2022

ISBN: (纸本)9789819918935

The proceedings contain 70 papers. The special focus in this conference is on Future learning Aspects of Mechanical Engineering. The topics include: Importance of Performance and Emission Characteristics in Biodiesel;Experimental Investigation of Parallel Fan Arrangement for Variable Air Volume in HVAC Systems;comparison of Basalt/Kevlar/Glass Fibre Duralumin Laminate-Reinforced Composite Using Nanoclay Filler;comprehensive Study on Solar Adsorption Cooling System;absorption Cooling System Powered by a Low Concentration Collector: A Case Study;analyzing the Factors Influencing the Market Feasibility of Alternative Fuel-Based Vehicles;hydrogen as a Fuel For Power Generation—A Review;mix Oil Biodiesel Blend’s Performance Characteristics with Energy Audit;portable Solar Stirling Engine;analyzing Barriers of Industry 4.0 Enabled Sustainable Manufacturing to Achieve Circular Economy;prioritization of Factors for Robust Manufacturing System: A Case of Disk Brake;maintaining Comfort Air Conditioning System Inside a Four Wheeler Using Phase Change Material;comparative Study of Phenotypic and Genotypic Methods for Biofilm Detection on Medical Devices: An Empirical Approach;synthesis and Analysis of Vital Social Sustainability Indicators Using Pareto Analysis;numerical Analysis of Cavity Flow at Different Angles of Attack;investigation of Carbon Nanotubes and Titanium Dioxide Doped Biodiesel on the Performance and Emission Characteristics of Four-Stroke Diesel Engine;a Study of the Effects of Low Quantity Lubrication on machine Efficiency;a Time Saving Image Segmentation Technique and Its application to Detect Cotton Leaf Spot;COMSOL Multiphysics Simulation of Microwave System-Based Household Welding Approach;Influence of Nanoparticles in application of Solar Collector-Based Energy Harvesting (SCBEH): A Review;a Review on Compliant Microgripper.

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Research on Decision Fusion Model for Driving Unmanned Logistic Vehicles Based on Complex Road Conditions 3

Research on Decision Fusion Model for Driving Unmanned Logis...

引用

3rd international conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Zhang, Yong-Jie Key Laboratory of Intelligent Connected Vehicle Application Technology of Guangxi University GuangXi Vocational Normal University Education Department of Guangxi Zhuang Autonomous Region Guangxi Nanning China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Traditional unmanned logistics vehicles mainly adopt a driving strategy of running along the track, which cannot adapt to complex roads and traffic. This paper introduces a fusion decision model for the behavior of unmanned logistics vehicles in complex road conditions, which can support intersections with traffic lights and complex traffic scenarios with multiple vehicles and multiple pedestrian flows. The paper implements a multi-state machine behavior fusion decision model for unmanned logistics vehicles by separately modeling the vehicle, pedestrian, and navigation situations at intersections with finite state machines, and then implementing model fusion through a fusion decision model. The paper use test vehicles to test in an indoor open road test site, and the experimental results show that the model can effectively avoid unmanned logistics vehicle blockage and route deviation at intersections, improve unmanned logistics vehicle intersection passage efficiency, and solve the technical problem that the traditional unmanned logistics vehicle cannot adapt to complex traffic road conditions. © 2023 SPIE.

关键词： Decision making

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3rd Biennial international conference on Future learning Aspects of Mechanical Engineering, FLAME 2022