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Human Pose Estimation for Fitness Exercise Movement Correction 24

Human Pose Estimation for Fitness Exercise Movement Correcti...

引用

24th International Electronics Symposium, IES 2022

作者： Rahmadani, Atharian Dewantara, Bima Sena Bayu Sari, Dewi Mutiara Politeknik Elektronika Negeri Surabaya Dept. of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

Based on computer vision technology, this research suggests an application to identify and assess a fitness practitioner's movements. Several fitness movements such as lifting weights, squat jumps, and pull-ups that are very beneficial for health and body fitness become the main movement for body building. However, those kinds of activities may be very dangerous if done incorrectly. Based on the problem, we developed an application based on computer vision to recognize and correct the pose accuracy of fitness practitioners by using input in the form of videos that record the movements of fitness practitioners continuously. To categorize the many forms of fitness sport movements, this system uses the support vector machine (SVM) method. On the monitor screen, the classification results will be visible. The result shows that the accuracy of the system is 96.87% by using SVM with the Radial Basis Function (RBF) kernel type and can make corrections to four types of fitness movements with a testing accuracy of 90.62%. © 2022 IEEE.

关键词： Sports

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Comparison of Bankruptcy Prediction Models Using Support Vector Machine and Artificial Neural Network 11

Comparison of Bankruptcy Prediction Models Using Support Vec...

引用

11th Electrical Power, Electronics, Communications, Control, and informatics Seminar, EECCIS 2022

作者： Sulistiani, Ita Widodo Nugraheni, Murien State University of Jakarta Dept. of Informatics and Computer Engineering Education Jakarta Indonesia

ISBN: (数字)9781665406482

ISBN: (纸本)9781665406482

In making financial decisions, many critical factors need to be considered, including the bankruptcy potential of the company. As a result, several bankruptcy prediction models, mainly using machine learning approaches, have been created. Based on relevant research, Support Vector Machine (SVM) and Artificial Neural Network (ANN) often provide the best performance in predicting companies' bankruptcy. On the other hand, both algorithms still have drawbacks, such as SVM is quite sensitive to feature selection, and ANN is prone to overfitting. In addition, many studies ignored the imbalance problems in the bankruptcy dataset, causing the prediction models to be prone to bias. Therefore, this research aimed to compare the performance of the SVM and ANN models in predicting companies' bankruptcy. To overcome the problem of overfitting and feature selection, we utilized the Principle Component Analysis (PCA) dimensionality reduction technique. In addition, for handling the imbalanced dataset problem, we used the Borderline Synthetic Minority Oversampling Technique (BSM). The data used in this study were gained from the annual financial statements of bankrupt and non-bankrupt public companies in Indonesia during the 2010 to 2019 years period. Based on the research, the ANN-BSM-PCA combination model with {PC =16} provided the best accuracy, precision, and recall scores of 0.96, 0.934, and 0.988, respectively. Therefore, the ANN-BSM-PCA with {PC =16} was the best model in the case of predicting companies' bankruptcy. © 2022 IEEE.

关键词： Support vector machines

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Leveraging the Pre-Trained Whisper Model and Levenshtein Distance for Audio Buginesse Transcription

Leveraging the Pre-Trained Whisper Model and Levenshtein Dis...

引用

2024 Beyond Technology Summit on informatics International Conference, BTS-I2C 2024

作者： Mukmin, Amirul Adnan Jalil, Abdul Pallawabonang, Mahabintang Prakasa, Esa Yuyun Handayani University of Makassar Dept. of informatics engineering Makassar Indonesia Handayani University of Makassar Dept. of computer system Makassar Indonesia Research center for data and information science National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331508579

This research aims to transcribe audio into text using the pre-trained Whisper model, focusing on Bugis, a low-resource language. A key challenge in transcribing local languages like Bugis is the limited availability of language model. To address this, we implemented an algorithmic correction approach that does not require extensive datasets or computationally demanding customizations. The Whisper model provides an initial transcription, serving as a foundation;however, the output often lacks accuracy due to the absence of specific adjustments for Bugis. To enhance accuracy, we applied the Levenshtein distance algorithm to correct word errors by comparing transcription results with entries in a Bugis language dataset based on similarity. Although this method is not fully precise and depends on the completeness of the reference dataset, evaluation results indicate that it effectively lowers the word error rate (WER) and improves transcription accuracy. This research produces SRT files containing transcripts that have been corrected using the Levenhstein distance algorithm, which not only improves accessibility but also contributes to the preservation of Bugis language through digital media. This approach is expected to provide a practical solution to improve transcription accuracy and support more accurate documentation in Bugis language preservation efforts. © 2024 IEEE.

关键词： Speech recognition

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EMP-GAN: Encoder-Decoder Generative Adversarial Network for Mobility Prediction

EMP-GAN: Encoder-Decoder Generative Adversarial Network for ...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Bang, Sammy Yap Xiang Raza, Syed M. Yang, Huigyu Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665494274

Ultra-dense cell deployments in Beyond 5G and 6G result in extensive overlapping between cells. This makes current reactive handover mechanism inadequate due to availability of multiple strong signals at a position. Moreover, recently proposed predictive mobility management schemes are also not suitable as they may lead to unnecessary handovers. A predictive path-based mobility management scheme can solve these issues, but forecasting User Equipment (UE) paths with high accuracy is a challenging task. This paper proposes Encoder-Decoder Generative Adversarial Network (EMP-GAN) for forecasting multi-step ahead UE path. EMP-GAN architecture consists of generator and discriminator neural networks, where the generator predicts mobility (next multi-step target sequence) and the discriminator classifies between the predicted target sequence and the ground truth in adversarial learning. Besides adversarial learning, feature matching and fact forcing training methods are employed for fast convergence of GAN and performance improvement. EMP-GAN is evaluated on mobility dataset collected from the wireless network of Pangyo ICT Research Center, Korea, and results show that it outperforms state-of-the-art prediction models. In particular, EMP-GAN achieves 95.55%, 94.70%, 93.50%, and 92.39% accuracies for 3, 5, 7, and 9-step predictions, respectively. © 2023 IEEE.

关键词： Generative adversarial networks

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Grammar Error Correction in Indonesian Sports News: Comparing the Performance of Pre-Trained T5 and BART Models

Grammar Error Correction in Indonesian Sports News: Comparin...

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2024 Beyond Technology Summit on informatics International Conference, BTS-I2C 2024

作者： Adnan, Muh Prakasa, Esa Yuyun Umar, Najirah Nasrullah Sadli, Adi Anatasya, A. Edeth Fuari Hazriani Razak, Mashur Handayani University of Makassar Dept. of informatics engineering Makassar Indonesia Research center for data and information science National Research and Innovation Agency Bandung Indonesia Handayani University Makassar Dept. of computer system Makassar Indonesia

ISBN: (纸本)9798331508579

This research compares the performance of two pre-trained models based on the transformer, namely text-to-text transfer transformer (T5) and bidirectional and auto-regressive transformer (BART), through a fine-tuned for correcting grammar in Indonesian sports news texts. We used vennify/t5-base-grammar-correction and onionLad/grammar-correction-bart-base, was trained using a dataset of 10,075 sentences from Indonesian language sports news sites. We tested the model performance using bilingual evaluation understudy (BLEU), google's language evaluation understanding (GLEU), training loss, and validation loss. The results show that BART was more efficient in learning data patterns and has better generalization ability, supported by BLEU and GLEU scores of 0.8560 and 0.9655. Meanwhile, T5 maintains simpler sentence structures, supported by BLEU and GLEU scores of 0.8120 and 0.9564. BART excels at handling more complex contexts, making it more effective at accurately correcting errors in dynamic sports news. Both models have their respective advantages, with BART being better at correcting detailed grammatical errors, while T5 offers consistent correction of more simple sentence structures. © 2024 IEEE.

关键词： Error correction

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Feature Pool Exploitation for Disease Detection in Fundus Images 17

Feature Pool Exploitation for Disease Detection in Fundus Im...

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Xiang Bang, Sammy Yap Le, Kim-Ngoc Thi Le, Duc-Tai Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Retinal fundus diseases without immediate diagnoses and treatment may lead to serious consequences such as permanent visual impairment. Recently, many machine learning (ML) and deep learning (DL) models have been introduced for fundus image classification. However, those heavy models require high-end graphics processing units for training and testing, thus not suitable for real-case usage in fundus cameras with limited computation power. In this paper, we demonstrate the effectiveness of our proposed two-block model, Feature Exploitation Lightweight network (FEL-net) which consists of a Feature Exploitation Block (FEB) and Lightweight Classification Block (LCB) by comparing it with other DL models. The experiment was carried out on a dataset of 21, 697 fundus images and our model achieves 99% binary classification accuracy. The proposed robust FEB is used to generate a refined feature pool for fundus images to build an efficient ML classifier that can distinguish fundus images with disease from the normal case with high accuracy and low running time. © 2023 IEEE.

关键词： Feature extraction

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A Procedure with MySQL Python for Developing Data Warehouse and Analytics Using Data from a Greek Soft Drinks Company

A Procedure with MySQL Python for Developing Data Warehouse ...

引用

International Conference on Applied Mathematics & computer Science (ICAMCS)

作者： Sotirios Tsakiridis Alkiviadis Tsimpiris Athanasios Angeioplastis Nikolaos Papaioannou Paris Mastorocostas Dimitrios Varsamis Dept. Computer Informatics and Telecommunications Engineering International Hellenic University Serres Greece Dept. Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (数字)9798350363807

ISBN: (纸本)9798350363814

The research focuses on the process of creating a data warehouse to meet the decision-making needs of a Greek beverage company. The data cover the period from 2018 to 2022. The developed data warehouse schema follows the star schema, with one fact table and five-dimension tables. Based on this structure, a web application was developed in PHP, which displays reports, charts, and analyses on the company's performance through interactive queries. Additionally, prediction functions tailored to the company's data were developed in Python. The process followed, the queries for creating the data warehouse, as well as the commands for creating analyses and predictions, serve as a useful guide for other companies that wish to upgrade the capabilities of their ERP systems.

关键词： Costs Data analysis Profitability Decision making Stars Companies Production Data warehouses Strategic planning Market research

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The Deep Learning-based Intelligent System for Extracting Information from e-KTP 2

The Deep Learning-based Intelligent System for Extracting In...

引用

2nd International Conference on Software engineering and Information Technology, ICoSEIT 2024

作者： Fuadi, Zaki Muchtar, Kahlil Subianto, Muhammad Riza, Hammam Oktiana, Maulisa Nizamuddin Universitas Syiah Kuala Department of Informatics Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Jakarta10340 Indonesia

ISBN: (纸本)9798350317503

This research implements an Intelligent Information System for Text Recognition on Indonesian Identity Cards (eKTP) based on Deep Learning architecture. The aim of this research is to develop a system capable of accurately and efficiently detecting and recognizing text on e-KTP images. The first phase of this research involves selecting ideal images from the e-KTP dataset. Subsequently, pre-processing is conducted to crop the image sides and merge them with background images to create a more diverse dataset and obtain the e-KTP image masks as training data. After selecting the ideal images, the total training data amounts to 144 images. The U-Net architecture is chosen as the deep learning method for image segmentation in this research, while for text detection and recognition, two neural networks, CRAFT and TRBA (TPS-ResNet-BiLSTM-Attention), are employed. The testing, based on accuracy values, Dice coefficients, and IoU for the segmentation process, results in a U-Net model accuracy of 99.52%. Text detection and recognition follow confidence values. © 2024 IEEE.

关键词： Intelligent systems

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Generating Automated Assistance Mechanism in Android Programming Self-learning System Using Automatic Testing Tools

Generating Automated Assistance Mechanism in Android Program...

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2023 International Conference on Innovation and Intelligence for informatics, Computing, and Technologies, 3ICT 2023

作者： Syaifudin, Yan Watequlis Liliana, Dewi Yanti Kaswar, Andi Baso Fatmawati, Triana Funabiki, Nobuo Siradjuddin, Indrazno State Polytechnic of Malang Dept. of Information Technology Malang Indonesia State Polytechnic of Jakarta Dept. of Informatics Engineering Jakarta Indonesia State University of Makassar Dept. of Computer Engineering Makassar Indonesia Okayama University Dept. of Elec. and Comm. Engineering Okayama Japan State Polytechnic of Malang Dept. of Electrical Engineering Malang Indonesia

ISBN: (纸本)9798350307771

Nowadays, computer-assisted learning (CAL) has experienced a significant increase in demand for self-learning purposes with the mechanism of automated assistance to facilitate assessments and provide instant feedback to learners. Using automatic testing, test cases can be executed repeatedly, providing informative feedback to assist students in correcting the written code referring to the assignment specifications. This research experiences an automated testing mechanism in an Android programming self-learning system to serve several important functions for learning experience enhancement. By adopting the TDD method and utilizing automatic software testing tools, the testing process combines unit testing with JUnit and integration testing with Robolectric. It ensures the quality and functionality of the application under test based on learning assignment's specifications. Utilizing the assertion methods realizes the code verification referring to expected behavior and potentially produces informative feedback that can be captured to provide automated assistance. Evaluation through the test code implementation in the Basic Application learning topic shows that automatic testing tools cover the automated assistance feature in the validations of project configuration and UI components. © 2023 IEEE.

关键词： Specifications

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A Comparative Analysis between Sentiment Analysis Methods in Stock Prediction of IoT-based companies 7

A Comparative Analysis between Sentiment Analysis Methods in...

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7th World Symposium on Communication engineering, WSCE 2024

作者： Pasoi, Georgia E. Talattinis, Kyriacos A. Memos, Vasileios A. Goudos, Sotirios K. Sarigiannidis, Panagiotis G. Ishibashi, Yutaka Psannis, Konstantinos E. University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Aristotle University of Thessaloniki Dept. of Physics Thessaloniki Greece University of Western Macedonia Dept. of Electrical and Computer Engineering Kozani Greece Aichi Sangyo University Faculty of Business Administration Dept. of Business Management Aichi444-0005 Japan

ISBN: (纸本)9798331542825

Predictions in financial markets have attracted much of scholars' interest. Since the stock market is generally defined as dynamic and volatile due to the affect of the well-known "Internet of Things"(IoT). It is a fact that IoT significantly impacts the financial market and stocks in various ways. Therefore, numerous researchers have attempted to detect patterns regarding the way financial markets react to various external factors. In the last decade, the integration of machine learning and sentiment analysis has appeared to be an effective approach for improving IoT-based stock price predictions in the financial field. This paper intends to propose an approach involving financial news as a source of information for forecasting the stock movement of noteworthy IoT-related companies, employing Big Data Analytics (BDA), Machine Learning, and Sentiment Analysis methods as well. A comparative analysis is conducted between lexicon-based and machine learning-based methods of sentiment analysis about their performance, particularly in the domain of IoT-related stock market prediction. © 2024 IEEE.

关键词： Decentralized finance

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