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Big Data as a Tool for Analyzing Academic Performance in Education 1

引用

11st World Conference on Information systems and Technologies (WorldCIST)

作者： Ayala-Chauvin, Manuel Chucuri-Real, Boris Escudero-Villa, Pedro Buele, Jorge Univ Indoamer Ctr Invest Ciencias Humanas & Educ CICHE Ambato 180103 Ecuador Univ Indoamer Fac Ingn Ind & Prod Carrera Ingn Ind Ambato 180103 Ecuador

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

Educational processes are constantly evolving and need upgrading according to the needs of the students. Every day an immense amount of data is generated that could be used to understand children's behavior. This research proposes using three machine learning algorithms to evaluate academic performance. After debugging and organizing the information, the respective analysis is carried out. Data from eight academic cycles (2014-2021) of an elementary school are used to train the models. The algorithms used were Random Trees, Logistic Regression, and Support Vector Machines, with an accuracy of 93.48%, 96.86%, and 97.1%, respectively. This last algorithmwas used to predict the grades of a new group of students, highlighting that most students will have acceptable grades and none with a grade lower than 7/10. Thus, it can be corroborated that the daily stored data of an elementary school is sufficient to predict the academic performance of its students using computational algorithms.

关键词： Big Data Academic Performance Education Support Vector Machines

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Enhancing Dysarthria Detection: Harnessing Ensemble Models and MFCC 1

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Jothieswari, J. Valli, T. Manicka Sundara Suguna, S. Madurai Kamaraj Univ Madurai India Govt Med Coll & Hosp Chithambaram 628002 Cuddalore India Sri Meenakshi Govt Arts Coll Women Madurai India

ISBN: (数字)9789819713295

ISBN: (纸本)9789819713288;9789819713295

A dysarthria involves difficulties with articulation, respiration, resonance, phonation, and prosody. Dysarthria management and treatment can be guided by an early and accurate diagnosis. Speech therapists traditionally use perceptual assessments to diagnose, which can be subjective. An objective analysis of speech features can be performed using automated methods based on machine learning. An extensive publicly available dataset, the TORGO database, is used to classify dysarthric speech. From recordings of Parkinson's dysarthric and control subjects, melt-frequency cepstral coefficients (MFCCs) are extracted. Normalization of the data is performed, and training and test sets are separated. In this paper, the performance of artificial neural networks (ANNs), ensemble convolutional neural networks (CNNs), and ensemble artificial neural networks (ANNs) for dysarthria detection is evaluated. As a result of the research, it has been able to develop diagnostic and assessment tools for dysarthria, improving the quality of life for people with this condition and providing them with some hope for early intervention. This work highlights the promising potential of automated speech analysis for dysarthria detection by employing state-of-the-art machine learning techniques.

关键词： Dysarthria Speech detection Mel-frequency cepstral coefficients (MFCC) Torgo database Ensemble artificial neural network (EANN)

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Distribution-Specific Augmentation for Domain Generalization 9th

Distribution-Specific Augmentation for Domain Generalization

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Goerttler, Thomas Schmidt, Lukas Obermayer, Klaus Tech Univ Berlin Chair Neural Informat Proc Berlin Germany Bernstein Ctr Computat Neurosci Berlin Berlin Germany

ISBN: (纸本)9789819735556;9789819735563

The generalization capability of deep neural networks usually deteriorates when the data changes distribution during test time. One type of such distribution shift is domain shift, where different domains are responsible for changing the underlying distribution of the associated data. This study proposes distribution-specific augmentation to mitigate domain generalization in multi-class image classification. Our proposed technique focuses more on sampling data related to specific classes. We do this assuming that class-related features are less likely to change significantly. To achieve this, we employ a background modeling technique called Robust Principal Component Analysis-Principal Component Pursuit (RPCA-PCP), which separates features specific to each class from the overall data. We assess the method on a simulated dataset and the iWildCam 2020 dataset. While the proposed method is applied successfully to the synthetic dataset, it only marginally improves overall test accuracy performance on the iWildCam dataset. However, the method highlights the dichotomy between class-relevant and irrelevant distributions in the data, which could inspire future research building on the ideas presented.

关键词： Domain generalization Augmentation Foreground detection

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Detection of Racism on Multilingual Social Media: An NLP Approach 1

引用

11st World Conference on Information systems and Technologies (WorldCIST)

作者： El Miqdadi, Ikram Kharroubi, Jamal Nikolov, Nikola S. Univ Sidi Mohamed Ben Abdellah Lab Intelligent Syst & Applicat Fes Morocco Univ Limerick Dept CSIS Limerick Ireland

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

This paper presents a comparison between various text vectorization andmachine learning algorithms for solving the problem of detection of racism on multi-lingual social media. We train classification models on Facebook comments and tweets in three different languages: English, French and Arabic. Our findings suggest that for the English-language comments, the combination of KNN with TF-IDF works best with an accuracy of 78.34%, while for French, the use of the SVM classifier with BOWprovides an accuracy of 82.56%. For Arabic we obtain an accuracy of 91.13% when KNN is coupled with BOW. Overall, our results suggest that the combination of SVM and TF-IDF is the best choice for detection of racism on social media that contains content in English, French and Arabic at the same time. As part of this work, we also present a new annotated dataset of social media comments in three languages.

关键词： NLP machine learning detection of racism

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Impact of Feature Normalization Techniques for Recognition of Speech for Mathematical Expression 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Kherdekar, Vaishali A. Naik, Sachin MIT Arts Commerce & Sci Coll Pune Maharashtra India Symbiosis Int Deemed Univ SIU SICSR Pune Maharashtra India

ISBN: (数字)9789819713134

ISBN: (纸本)9789819713127;9789819713134

Speech recognition is an important aspect of Human-Computer Interaction (HCI). Speech recognition systems can be implemented with the help of speech signal patterns. In pattern recognition, feature selection and feature extraction are important phases. The performance of the model depends not only on the feature selection and feature extraction but also on feature preprocessing. In our existing work, we have proposed a hybrid feature extraction algorithm for the recognition of speech for audio mathematical expression. In this paper, we have studied five feature normalization techniques used for audio processing. We compared five feature normalization functions with time domain, frequency domain, and cepstral domain features using Audio Mathematical Expression Recognition (AMER) dataset. Six types of audio mathematical expression are classified using five feature normalization functions. The observations indicate that the standard scaler and robust scaler give more accuracy and quantile transformer scaler gives less accuracy. The combination of feature normalization, feature extraction, and classification complies with higher accuracies.

关键词： Speech recognition Feature normalization Pattern recognition Audio mathematical expression recognition

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Proposal for a System for Characterizing the Functioning of a Phototherapy Lamp

Proposal for a System for Characterizing the Functioning of ...

引用

Intelligent systems Conference

作者： Serpa-Andrade, Luis Garcia-Velez, Roberto Mata-Quevedo, Jean P. Univ Politecn Salesiana GIHEA Cuenca 010102 Ecuador Univ Catolica Cuenca Azogues 030101 Ecuador

ISBN: (纸本)9783031664274;9783031664281

The standardization of light-emitting treatment systems and the need to characterize the medical devices used to treat neonatal jaundice are important. In this regard, it is true that light-emitting devices may vary in their physical characteristics and feeding requirements, and therefore, it is necessary to study and standardize their behavior in order to optimize treatments and ensure their efficacy and safety. In this context, it is important to have laboratory tools to measure and characterize medical devices used for the treatment of neonatal jaundice. In this case, a device capable of measuring the temperature, humidity and number of lumens of phototherapy lamps used for this purpose is mentioned. Such tools make it possible to assess the quality and efficacy of the lamps, identify possible failures, and make adjustments to improve their performance. In addition, the importance of having software to manage the device and review the results is mentioned. Such tools can be very useful in educational settings, as they allow students and teachers to access the data and results of the analyses performed with the device, which can facilitate the understanding of the principles and applications of phototherapy in neonatal jaundice. This work covers the standardization and characterization of medical devices used in the treatment of neonatal jaundice, which is essential to ensure their efficacy and safety. In this sense, laboratory tools such as the aforementioned device can be very useful to evaluate and optimize the performance of phototherapy lamps, and the use of software can facilitate the learning and understanding of the principles and applications of phototherapy in educational settings.

关键词： Phototherapy lamp Academic testing device Temperature Humidity and number of lumens measurement

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Planning and Scheduling of Robotic Welding Processes in the Rolling Stock Industry: A Literature Review 7th

Planning and Scheduling of Robotic Welding Processes in the ...

引用

7th International Conference on Logistics Operations Management (GOL)

作者： Payami, Kimia Lehoux, Nadia Cloutier, Caroline Morin, Michael Univ Laval Dept Mech Engn Quebec City PQ Canada Univ Laval Dept Operat & Decis Syst Quebec City PQ Canada

ISBN: (纸本)9783031686337;9783031686344

Robotized welding processes in the manufacturing industry play a crucial role in enhancing competitiveness through automation, adaptability, and increased productivity. To optimize welding parameters, modeling approaches have gained significance, enabling users to simulate welding experiments and determine appropriate settings. With the growing need for reduced development phases and costs while maintaining quality standards and production volumes, flexible and robust manufacturing technologies are essential. In this paper, we present a literature review highlighting best practices for welding processes. We address five research questions related to welding techniques, planning models, factors affecting welding processes, and performance indicators. Our findings reveal various models and techniques for planning robot operations, focusing on welding robots. Bythis review, we contribute to the development of effective strategies for optimizing robotized welding processes, leading to improved efficiency in manufacturing systems.

关键词： Optimization models Robot welding process Welding techniques

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Improvement of Small Object Detection Effectiveness Based on Swin Transformer 7th

Improvement of Small Object Detection Effectiveness Based on...

引用

7th International Conference on Green Technology and Sustainable Development

作者： Dung Nguyen Van-Dung Hoang Van-Tuong-Lan Le Ngoc-Thuy Nguyen Hue Univ Univ Sci Hue City Vietnam HCMC Univ Technol & Educ Ho Chi Minh City Vietnam Hue Univ Hue City Vietnam

ISBN: (纸本)9783031761966;9783031761973

This paper proposes a novel approach to improve performance and effectiveness of the detection transformer (DETR) model for small object detection. The proposed approach utilizes the Swin transformer as the backbone for the DETR model and applies a local attention mechanism. The Swin transformer possesses better multi-level representation capability than the conventional CNN backbones, which helps improve training efficiency and object detection capability. The Focal loss function supports the model converge quickly when dealing with imbalanced training datasets. Experimental results demonstrate that the DETR model using Swin transformer as the backbone and Focal Loss function achieves higher training performance and better small object detection capability than the DETR model using conventional CNN backbones. The model almost converged at Epoch 50th, much lower than the convergence rate of the original DETR model.

关键词： DETR Swin-Transformer focal loss object detection small object training performance

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A Hybrid Technique for English to Urdu Machine Translation Using Transfer Learning

A Hybrid Technique for English to Urdu Machine Translation U...

引用

Intelligent systems Conference

作者： Adrees, Muhammad Rabia, I. M. Arslan, Muhammad Aslam, Muhammad Martinez-Enriquez, A. M. Jahangeer, Asma Hanif, Noman Tenzsoft Gujranwala Pakistan Univ Engn & Technol Dept Comp Sci Lahore Punjab Pakistan CINVESTAV Dept Comp Sci Mexico City DF Mexico

ISBN: (纸本)9783031663284;9783031663291

Machine Translation is a technique that helps in overcoming the linguistic barriers often encountered in the global world. Multiple techniques are being employed for automatic machine translation. Encoder-decoder models are frequently being implemented to improve the performance of automatic translation systems. Convolutional Neural Network (CNN) is a novel approach used for extracting textual features from the dataset. Merging recurrent neural networks (LSTM and GRU) with CNNs has presented fruitful results when working with low-resource language translation like English-to-Urdu translation. To reduce the workload of the model, transfer learning is implemented by using the Fasttext word embedding model. To evaluate the models, BLEU, TER, METEOR precision, recall, and F-measure are the parameters which are calculated. It can be perceived from the results that hybrid models (i.e. a combination of LSTM and GRU with CNN) demonstrated better results when compared to the LSTM and GRU alone.

关键词： CNN English-Urdu translation Hybrid model Machine translation

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Functionality-Related Performance of Surface Microrelief Evaluation in Ultrasonically and Shot Peened Inconel 718 Alloy Manufactured by Laser Powder Bed Fusion Process 10th

Functionality-Related Performance of Surface Microrelief Eva...

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Lesyk, Dmytro Martinez, Silvia Mordyuk, Bohdan Grochala, Daniel Lamikiz, Aitzol Univ Basque Country Aeronaut Adv Mfg Ctr Zamudio 48170 Spain Natl Tech Univ Ukraine Laser Syst & Adv Technol Dept Igor Sikorsky Kyiv Polytech Inst UA-03056 Kiev Ukraine NAS Ukraine Phys Principles Surface Engn Dept GV Kurdyumov Inst Met Phys UA-03680 Kiev Ukraine West Pomeranian Univ Technol Mech Engn Dept PL-70310 Szczecin Poland Univ Basque Country Mech Engn Dept Bilbao 48013 Spain

ISBN: (纸本)9783031662676;9783031662683

In this study, the effects of the ultrasonic shot peening, multi-pin ultrasonic impact peening, and shot peening on the 3D surface texture parameters and functionality-related properties of additively manufactured Inconel 718 material were analyzed and compared. Inconel 718 alloy parts were produced by a laser powder bed fusion (LPBF) additive manufacturing process with a build angle of 90 degrees using a stripe scanning strategy. The influence of severe surface plastic deformation techniques on surface morphology evolution and areal functional parameters/indices to analyze the wear and lubrication behavior was studied via confocal scanning microscopy. It was observed that the applied mechanical surface post-treatments decreased the average surface roughness parameters. The values of the area surface roughness parameters are the highest for shot peening and lowest for multi-pin ultrasonic impact peening. The results showed that the ultrasonic finishing post-treatments provided a decrease in the area surface roughness Sa parameters magnitudes by 3-6 times as compared to the LPBF-built parts. The formation of surface microrelief by peening techniques leads to improved functionality-related performance in the LPBF-fabricated superalloy parts.

关键词： LPBF post-processing Inconel 718 ultrasonic shot peening multi-pin ultrasonic impact peening shot peening 3D surface roughness functional parameters

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