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Transformer-Based Multilingual G2P Converter for E-Learning System 3rd

Transformer-Based Multilingual G2P Converter for E-Learning ...

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3rd international conference on Artificial intelligence in HCI (AI-HCI) Held as Part of the 24th international conference on Human-Computer Interaction (HCII)

作者： Liu, Jueting Ren, Chang Luan, Yaoxuan Li, Sicheng Xie, Tianshi Seals, Cheryl Atkins, Marisha Speights Auburn Univ Auburn AL 36849 USA Northwestern Univ Evanston IL 60208 USA

ISBN: (纸本)9783031056437;9783031056420

Phonetic transcription is an approach to represent speech sounds to specific symbols. The most common alphabet we used is the international Phonetic Alphabet (IPA), and the characters in the IPA are phonetic symbols. To support the phonetic transcription process in the phonetic exams of our linguistic E-learning system, we designed a machine translation tool that aims to translate English words to their phonetic formats. This progress can also be expressed as grapheme to phoneme (G2P). The Transformer model has been utilized to develop this G2P module. Also, to improve the functionality of the E-learning system, we trained multiple language models and generated a multilingual G2P translator. Moreover, we evaluated our G2P system by word error rate (WER) and phoneme error rate (PER) with edit distance.

关键词： E-learning international phonetic alphabet Grapheme-to-phoneme Transformer

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Fault Diagnosis of the Ball Screw Pairs in Electromechanical Actuators Based on Empirical Mode Decomposition and Symmetrized Dot pattern

Fault Diagnosis of the Ball Screw Pairs in Electromechanical...

引用

international conference of The Efficiency and Performance Engineering Network (TEPEN)

作者： Yin, Zhengyang Hu, Niaoqing Yang, Yi Chen, Ling Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China NUDT Lab Sci & Technol Integrated Logist Support Changsha 410073 Peoples R China

ISBN: (纸本)9783031261954;9783031261930;9783031261923

Recently, the development of many critical systems around the word have put forward the demand for high reliability and high security electromechanical actuators (EMAs). However, the reliability of EMA is strongly restricted by its complicated working condition. Therefore, it is necessary to conduct fault diagnosis of the critical components in EMA systems. Inspired by the existing research, this paper introduces empirical mode decomposition (EMD) and symmetrized dot pattern (SDP) into the fault diagnosis of the ball screw pair in EMA, which is one of the EMA components that are prone to failure. In this paper, the parameters required for SDP image generation are optimized according to the fault characteristic frequency of the ball screw pairs. Then, the data measured on the EMA test rig and the existing GoogLeNet neural network model are used to verify the effectiveness of the diagnosis method with relatively small sample size (100 images per label). The results show that the diagnosis method maintains high recognition accuracy (over 90%) at different screw shaft rotating speeds (90-390 rpm), and the accuracy of the model after parameter optimization is improved by about 1.5%. However, it can also be found from the experimental results that this diagnosis method cannot effectively identify the fault at low screw shaft rotating screw speeds, such as 30 rpm or 60 rpm.

关键词： Fault diagnosis Electromechanical actuator Empirical mode decomposition Symmetrized dot pattern Convolutional neural network

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Design and Implementation of Marketing Classification System Based on Artificial intelligence

Design and Implementation of Marketing Classification System...

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Electrical Engineering, Big Data and Algorithms (EEBDA), IEEE international conference on

作者： Shuo Tian Shanghai University Shanghai China

This paper mainly introduces some machine learning methods used in the field of data mining. The method of data mining is discussed by taking market segmentation algorithm as an example. This paper presents an improved support vector machine method. By judging the similarity between each sampling point and its closest class, the classifier is obtained by training it with support vector machine. A method based on stepwise approximation method is proposed to solve the problem of too large size of KNN training samples. The experimental results show that non-SVM is superior to SVM in classification accuracy and classification speed. In addition, this project will also adopt artificial intelligence technology to build a data analysis system for high-end wine, analyze, compare and match massive information data of high-end wine through data mining, and discover the comparison information and market potential of main ingredients in high-end wine. It is compared with the chemical composition of high-grade red wine, and the relevant marketing treatment of high-grade red wine is obtained. It can help high-grade red wine manufacturers to analyze the composition content and quality of high-grade red wine.

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A Novel DAAM-DCNNs Hybrid Approach to Facial Expression recognition to Enhance Learning Experience 23rd

A Novel DAAM-DCNNs Hybrid Approach to Facial Expression Rec...

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23rd international conference on Computational Science, ICCS 2023

作者： Alfred, Rayner Pailus, Rayner Henry Obit, Joe Henry Lim, Yuto Sukirno, Haviluddin Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia School of Information Science Japan Advanced Institute of Science and Technology Access 1-1 Asahidai Ishikawa Nomi923-1292 Japan Department of Informatics Universitas Mulawarman Kalimantan Timur Samarinda Indonesia

ISBN: (纸本)9783031360268

Many machine learning models are applied on facial expression classification and there are three main issues affecting the performance of any algorithms in classifying emotions based on facial expressions, and these issues include image illumination, image quality and partial features recognition. Many approaches have been proposed to handle these issues. Unfortunately, one of the main challenges in detecting and classifying facial expression process is minimal differences of features between different types of emotions that can be used to differentiate these different types of emotions. Thus, there is a need to enrich each type of emotion with more relevant extracted features by having a more effective approach to extract features that can be used to represent each type of emotions more effectively and efficiently. This work addresses the issue of improving the emotion recognition accuracy by introducing a novel hybrid approach that combines the Depth Active Appearance Model (DAAM) and Deep Convolutional Neural Networks (DCNNs). The proposed DAAM and DCNNs model can be used to assist one in identifying emotions and classify learner involvement and interest in the topic which are plotted as feedback to the instructor to improve learner experience. The proposed method is evaluated on two publicly available datasets namely, JAFFE and CK+ and the results are compared to the state-of-the-art results. The empirical study showed that the proposed DAMM-CNNs hybrid method managed to perform the face expression recognition with 97.4% for the JAFFE dataset and 96.9% for the CK+ dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Contrastive Learning

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Human Pose Estimation Based on GAN and DCGAN Models 3

Human Pose Estimation Based on GAN and DCGAN Models

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3rd international conference on machine Learning, Big Data and Business intelligence (MLBDBI)

作者： Ma, Jiaming Li, Jiaqi Feng, Shenglu Donghua Univ Sch Comp Sci & Technol Shanghai Peoples R China Hebei Univ Technol Sch Artificial Intelligence & Data Sci Tianjin Peoples R China Wuhan Univ Sch Elect Engn Wuhan Peoples R China

ISBN: (纸本)9781665417907

As an important application based on the field of computer vision, the various human movements and gestures can be reconstructed by detecting the key articulation points of the human body. It is mainly used in human behavior recognition, human-computer interaction, and attitude tracking. However, the current human pose estimation models have many challenges, such as difficulty detecting the non-typical articulation points of the human body and inaccurate locating of the extremities. They are prone to error or lack of information in complex situations. This paper proposed GAN and DCGAN models to tackle this issue, which can improve the accuracy of human posture prediction. This paper mainly focuses on the contribution of Generative Adversarial Network to the detection of human key articulation points, revising the original model posture and obtaining a model that is closer to the real posture of the human body. The experimental results demonstrated that the model's accuracy is improved to a certain extent after using the DCGAN model. Furthermore, we note that in most cases, the performance of the proposed model is superior to others.

关键词： Human pose estimation Key articulation points detection DCGAN model Experimental analysis

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AI for Learning: Views on Impacts to Teachership in the era of Artificial intelligence 3

AI for Learning: Views on Impacts to Teachership in the era ...

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3rd European conference on the Impact of Artificial intelligence and Robotics (ECIAIR)

作者： Saukkonen, Juha Huhtala, Mari Rantonen, Mika Vaara, Elina JAMK Univ Appl Sci Business Sch Management Jyvaskyla Finland JAMK Univ Appl Sci Jyvaskyla Finland

ISBN: (纸本)9781914587238

Artificial intelligence (AI) is an umbrella term for systems that can act in cognitive processes in a human-like and human-enhancing manner, e.g., in learning, problem solving, and pattern recognition. According to models of technology adoption, several factors influence the actual implementation of a new system within an organization and in an individual's professional practice. These factors include e.g. job relevance, demonstrable results, individual experience with technology, and voluntariness to adopt the new system. This research studies employees' views and expectations of AI applicability and its impact to teachership within a Finnish higher education institution (HEI). Survey data was collected from different schools and units from all hierarchical layers of the HEI, a University of Applied Sciences. Views on AI were assessed in relation to the core tenets of a teacher ' s professional guidelines as expressed in the Comenius' Oath. This research contributes to the AI research by shedding light on how people within the HEI evaluate the impacts of AI into their future operating environment, pointing out also the potential obstacles for AI adoption in this specific context.

关键词： artificial intelligence higher education human-machine interaction learning ethics teachership

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Proposing a Genetic Algorithms-Based Data Selection Method for Imbalanced Medical Datasets 15th

Proposing a Genetic Algorithms-Based Data Selection Method ...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Salmi, Mabrouka Atif, Dalia Abraham, Ajith Ventura, Sebastian National Higher School of Statistics and Applied Economics Tipaza Algeria University of Cordoba Cordoba Spain University Center Tipaza Algeria School of Artificial Intelligence Sai University Chennai India

ISBN: (纸本)9783031810794

The class imbalance issue is evident in medical datasets, posing a hurdle for accurate predictive modeling. Maintaining the natural characteristics of medical data while mitigating this issue is paramount in ensuring the success of clinical decision support systems. Therefore, this study proposes a genetic algorithm-based data selection method (GA-DS) for imbalanced medical data reserving its distribution and characteristics. It optimizes the assignment of samples into train and test sets by improving the recognition of rare samples on test data. We presented three versions of the GA-DS, one unconstrained and two constrained (train-set >=50%, train-set = 70%) to ensure the reliability of the results. Evaluation of the GA-DS methods on three commonly used imbalanced medical datasets and comparison with SMOTE, random oversampling, and stratified random sampling demonstrated the superior performance of GA-DS by improving the sensitivity score on test data. Future work will investigate its performance on large and highly imbalanced data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Class imbalance data distribution data selection genetic algorithms

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Melody Extraction from MIDI Files Using AI-ML Techniques

Melody Extraction from MIDI Files Using AI-ML Techniques

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Innovation in Technology (INOCON), IEEE international conference for

作者： Navita Ashok Kumar Chanambam Mokaju Meitei Imaging & Forensics & Dept of Physics Center for Artificial Intelligence in Medicine Sharda University Greater Noida India

Development of Artificial intelligence and machine Learning (AI-ML) based automated tools for melody extraction from audio music files are severely handicapped due to the scarcity of proper musical annotation to act as labels for supervised learning. For many kinds of traditional music the melodies can be quite intricate, and detailed annotation is not only scarce but also quite difficult to create. MIDI music files on the other hand carry within them the notes of melody which if extracted can act as musical annotations for training supervised melody extraction tools. In this study, several machine Learning based classifiers were trained to determine whether a particular channel of MIDI file carries a melody or not. The six features used for training were Average TFIDF, Overlap, Number of Unique Notes, Note Duration, Note Velocity and Entropy. The testing accuracies obtained by Support Vector machine (SVM), Random Forest, Logistic Regression, and Artificial Neural Network (ANN) classifiers were 86.67%, 88.64%, 86.91% and 93.80% respectively, with ANN based method giving the best testing result.

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Development of Non-Conventional Method of Lissajous pattern for gear fault diagnosis using machine Learning Technique 3

Development of Non-Conventional Method of Lissajous pattern ...

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3rd international conference on Intelligent Robotics, Mechatronics and Automation Systems, IRMAS 2023

作者： Bankar, Akshay Wani, K.P. Department of Mechanical Engineering COEP Technological University Pune411005 India Automotive Research Association of India Pune410501 India

Gears are major elements in machine parts and automotive systems, to transmitting torque and energy between components. As gears are under severe loads and stresses, they can create different types of faults and defects over period, impacting their own performance and reliability. Fault detection is an essential part of gear maintenance because that allows potential issues to be detected before they induce failures or expensive breakdowns. Technicians can identify early indications of wear, imbalance, damage, or even other concerns that might compromise their performance or lead to premature failure by monitoring the condition of gears. The industry has conventionally used vibration signals, thermography, oil analysis, and visually inspecting to diagnose faults. These techniques also have significant limitations which can decrease their accuracy in detecting faults and the cost is also very high. To overcome these limitations, new methods such as Lissajous pattern developed for fault detection in gears. These techniques have proved impressive outcomes in fault detection which have been previously undetectable using Conventional methods. This study evaluates the accuracy, performance, and cost-effectiveness of conventional and non-conventional techniques to determine the best method for fault diagnosis. Once compared to the conventional method, the Lissajous method obtained better accuracy than conventional method. © Published under licence by IOP Publishing Ltd.

关键词： Failure analysis

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Overview of the Development of Origami-inspired Robot Arms 3

Overview of the Development of Origami-inspired Robot Arms

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3rd international conference on Sensing, Measurement and Data Analytics in the Era of Artificial intelligence, ICSMD 2022

作者： Liu, Yixue Yang, Laihao Sun, Yu School of Mechanical Engineering Xi'An Jiaotong University Xi'an China

ISBN: (纸本)9781665492812

Robotic arms have always been the enabling technology that promotes the development of human society and science technology, such as invasive surgery, intelligent manufacturing, aerospace engineering, etc. However, conventional robotic arms suffer from the disadvantages of limited DOFs, hefty weight, miniaturization difficulty, and poor human-machine interaction. The emerging soft robot arm is attracting attention for its excellent performance regarding human-machine interaction and degrees of freedom. As a novel paradigm of the soft robot arm, the origami-inspired robot arm not only reserves the advantages mentioned above but also is better at coping with more scenarios due to its inherent deformation ability. This study focuses on origami-inspired robotic arms, summarizes the commonalities of origami crease patterns capable of being used to design and make robotic arms, and addresses the state-of-the-art investigations on the driving method, intending to offer insights into the future direction of the development for origami-inspired robotic arms. © 2022 IEEE.

关键词： design driving method kinematics origami crease pattern origami-inspired robot arms

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