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Xception Based Transfer Learning Combined With Residual Block for Facial Emotion Recognition 4

Xception Based Transfer Learning Combined With Residual Bloc...

引用

4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Yoshino, Erio Hendry, Antoluis Kurniadi, Felix Indra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

Human emotion plays a crucial role in communication, making its recognition an essential task. In recent years, transfer learning and residual blocks have emerged as effective methods for improving image classification performance. This paper aims to develop an emotion recognition model by leveraging the Xception architecture and incorporating residual blocks from ResNet. To achieve this, we made modifications to both the model architecture and the residual block architecture. Additionally, we utilized two different validation datasets. The second validation dataset involved image augmentation techniques, introducing additional variations and noises. Our study demonstrates the benefits of integrating residual blocks into the Xception-based model, achieving a validation accuracy of 65.78% on the first dataset and 60.56% on the second, surpassing the accuracy of the standard Xception model. However, during experimentation, we observed that all tested models exhibited overfitting, with validation accuracy significantly lower than training accuracy, partly attributed to the limited number of images in the datasets. As for the future works, we intend to address dataset imbalance through image augmentation and implement dropout layers to prevent overfitting. © 2023 IEEE.

关键词： Emotion Recognition

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Enhancing Emotion Recognition using Contrastive Learning Representation of Paired Peripheral Physiological and EEG Signal 9

Enhancing Emotion Recognition using Contrastive Learning Rep...

引用

9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Laksana, I Kadek Perry Bagus Yonatan, Michiko Amaya Parimartha, Putu Agus Hongastu, Vanessa Aprily Parmonangan, Ivan Halim Diana Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350370126

Human emotions play a fundamental role in human-computer interaction, and accurately recognizing emotions is essential for enhancing user-friendly experiences in HCI applications. Emotions are complex and cannot be simply identified by a single modality, making multimodal approaches crucial. In this study, we propose a cross-modal representation learning approach for emotion recognition using peripheral physiological signals (PPS) and electroencephalogram (EEG). We focus on leveraging PPS, which is easier to obtain in real-life scenarios due to wearable technologies' availability. Through contrastive learning with paired EEG signals, we aim to enhance the capabilities of PPS representation and achieve comparable performance to EEG-based models in downstream unsegmented emotion recognition tasks. We utilized the DEAP dataset and low or high arousal category for the downstream task. The results of the study demonstrate the successful learning of PPS representations that closely resemble EEG signals representations, leading to comparable performance on downstream tasks. © 2023 IEEE.

关键词： Emotion Recognition

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Evaluation of *** Website in User Experience Perspective using User Experience Questionnaire (UEQ) 8

Evaluation of *** Website in User Experience Perspective usi...

引用

8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Lee, Aileen Angelica Rachmat, Ananta Mahardika Felice Ramadhani, Mirza School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

A lot of organizations now provide online services to assist their customers in the digital age. This includes websites that facilitate volunteering, such as ***. Nevertheless, despite the huge number of online platforms, it is not always simple and comfortable for people to use them. Users may experience issues with unclear information and unattractive website design. This user dissatisfaction tends to be caused by a user experience that is unsuitable for usage on the website. This paper aims to enhance the user experience (UX) on the Indorelawan website with the purpose of attracting and obtaining a larger number of volunteers in indonesia. In this research we will identify problems and barriers experienced by users when using the website, as well as to devise strategies and necessary improvements to enhance the UX. This study used the instrument User Experience Questionnaire (UEQ) survey for evaluate the website ***'s user experience (UX). UEQ is a mechanism used to determine and evaluate whether *** meets user expectations in terms of UX. Using UEQ responses from users of ***, it was determined that the platform fulfilled users' needs and expectations in every category. The analysis result is classified into 5 categories, with *** placing highest in the attractiveness category. But the novelty category on the *** website is lacking and needs to be improved. Overall, *** website has a good User Experience and it can help user feel at ease when utilizing the website. © 2023 IEEE.

关键词： Web Design

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K-Nearest Neighbor Algorithm for Heart Disease Detection: A Comparative Evaluation of Minkowski and Manhattan Distances 6

K-Nearest Neighbor Algorithm for Heart Disease Detection: A ...

引用

6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Prasetyo, Simeon Yuda Zain Nabiilah, Ghinaa Nabila Izdihar, Zahra Prabowo, Abram Setyo Ash Shiddiqi, Hafizh School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350358346

Cardiovascular disease, encompassing heart-related issues, is the leading global cause of mortality, claiming over 17.9 million lives annually according to the World Health Organization (WHO). This emphasizes the need for precise prediction and early intervention, as accurate diagnosis and timely action can significantly reduce this burden. In this study, we explore the application of the K-Nearest Neighbor (KNN) algorithm for heart disease prediction, with a specific focus on the Minkowski and Manhattan distance metrics. By employing a diverse dataset, we investigate the influence of different neighbor quantities (1, 3, 5, 7, and 9) and distance metrics, shedding light on the optimal configuration for accurate predictions. The results highlight that the KNN algorithm configured with 7 neighbors and utilizing the Manhattan distance metric achieves the highest accuracy at 91.304%, surpassing the Minkowski metric. This research underscores the potential of machine learning in healthcare and recommends further exploration of neighbor quantities and alternative distance metrics to continually enhance the accuracy and effectiveness of heart disease prediction models. These insights provide valuable contributions to the advancement of sophisticated heart failure prediction models, benefiting medical diagnostics and predictive modeling in the field of cardiology, ultimately working towards the reduction of the global heart disease burden. © 2023 IEEE.

关键词： Cardiology

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An Enhanced Approach for Divorce Prediction: Implementing XGBoost Machine Learning Model with Particle Swarm Optimization on the 54 Divorce Predictors Scale Dataset 6

An Enhanced Approach for Divorce Prediction: Implementing XG...

引用

6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Artanto, Brychan Chang, Frenrico Situmeang, Jason Made Meiliana Achmad, Said Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315639

Divorce is the final termination of the marital relationship which can be caused by many things and has been increasing over the past few decades. According to this case, our paper proposes a novel study about divorce prediction using the XGBoost algorithm optimized with Particle Swarm Optimization (PSO). With the aim to enhance the performance of the divorce prediction model and provide valuable insights for effective intervention and support. Our experiment was conducted on a comprehensive Gottman dataset that consists of 54 variables known as the Divorce Prediction Scale (DPS) based on Gottman Couple's Therapy and collected from 170 participants. With the tuned hyperparameter of XGBoost and PSO, our experiment shows the effectiveness of the divorce prediction model with the proposed method. Achieving an accuracy ranging from 98.823% to 99.411% and outperforming traditional methods also provides valuable insights into the divorce prediction area. This paper contributes to the field of divorce prediction by introducing a novel algorithm and the results show that DPS can be used to predict divorce. However, studies on this topic can be further developed by considering the amount of data and the country in which data were collected. © 2023 IEEE.

关键词： Forecasting

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Malang Historical Monument in HIMO Application with Augmented Reality Technology 1

引用

International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Alexander, Christoper Luis Sangalang, Christiano Ekasakti Sampurna, Jonathan Evan Maulana, Fairuz Iqbal Ramadhani, Mirza Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

History is a legacy found in every country, including Indonesia. In Indonesia, various kinds of historical heritage have values of the struggle of the Indonesian people. But unfortunately, the young generation today does not know and understand the historical values in Indonesia. If this is not handled immediately, the Indonesian people will not appreciate the hard work of the struggles of previous Indonesian heroes. One of the historical relics in Indonesia is the Hero Monument which is located in Malang City. But unfortunately, many young people are not familiar with this monument. For this reason, by using current technological developments, a 3D application is made that allows the younger generation to see and experience the Hero Monument in Malang City virtually. In this way, each young generation is limited to Malang City and from the provinces to the whole of Indonesia can recognize and understand the historical values that exist. Unity, Vuforia, and Blender technologies are used to develop applications. In addition, this application also ensures that it makes it easy for every user to use it to scan and display the monument virtually. With this application, it is hoped that the younger generation will be increasingly interested in understanding and understanding the historical values that exist in Indonesia. "A great nation is a nation that respects the services of its heroes" this is our guideline in developing the Himo application. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Augmented reality

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Exploring XGBoost as an Effective Machine Learning Algorithm for Stellar Spectral Data Classification in Astronomy

Exploring XGBoost as an Effective Machine Learning Algorithm...

引用

2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Yoshino, Erio Juarto, Budi Kurniadi, Felix Indra Bina Nusantara University School of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350339215

The discipline of astronomy has experienced a substantial metamorphosis due to the advent of contemporary telescopes, which facilitate the accumulation of extensive spectral information. The processing of data through traditional manual means has become unfeasible owing to its vast volume, thereby necessitating the adoption of automated classification. The advent of machine learning algorithms and artificial intelligence has enabled the efficient and accurate processing of spectral data, thereby facilitating a more comprehensive analysis of celestial objects. Prior research has investigated the efficacy of diverse machine learning algorithms in the context of stellar classification endeavors, as well as the significance of pre-processing spectral data. The present investigation centers on the utilization of XGBoost, a machine learning algorithm that exhibits the ability to handle heterogeneous data types and address imbalanced datasets, thus rendering it a viable option for classification endeavors. In this research, we found that Random Forest and XGBoost gave good result compared to other classifier. In contrast, reduction dimension using PCA are lowering the achieved result. © 2023 IEEE.

关键词： Machine learning

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Temporal Fusion Transformer for Multi Horizon Bitcoin Price Forecasting 9

Temporal Fusion Transformer for Multi Horizon Bitcoin Price ...

引用

9th IEEE Information Technology International Seminar, ITIS 2023

作者： Amadeo, Adriel Justin Siento, Joyceline Graciella Eikwine, Tesalonika A. Diana Parmonangan, Ivan Halim School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350306835

Bitcoin is one of the cryptocurrencies that have a large number of holders and a high transaction volume. Its price has experienced major fluctuation since its inception, resulting in relatively high price volatility. This has been a challenge for researchers over the past decade in forecasting its future price. In this study, we aim to address this problem by utilizing a neural network model called the Temporal Fusion Transformer (TFT). The TFT model uses the concept of multi-horizon forecasting to learn from historical data and predict future prices based on multiple time steps. Due to the numerous factors influencing Bitcoin's price, our study incorporates additional predictive data, such as Twitter sentiment, trends, and seasonality. The proposed method is trained using the TFT model with historical Bitcoin data from 2014 until 2020, then we performed transfer learning by using different past covariates as inputs. As a result, the proposed method in this study demonstrated the best performance, with a MAE of 0.05, a RMSE of 0.07, a MAPE of 5.92%, and a quantile loss of 0.03, surpassing other approaches. This makes it the state-of-the-art solution for highly volatile forecasting using a neural network model. © 2023 IEEE.

关键词： Forecasting

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Rice Varieties Classification Using Neural Network and Transfer Learning with MobileNetV2 4

Rice Varieties Classification Using Neural Network and Trans...

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Vania, Vanessa Setyadi, Anthony Widyatama, I Made Danendra Kurniadi, Felix Indra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

Rice plays a vital role in global food security, feeding over 8 billion people worldwide. The classification of rice varieties based on visual characteristics, such as color, shape, and texture, is crucial for quality inspection and efficient farming practices. However, existing classification methods heavily rely on subjective and time-consuming human visual inspection, leading to errors and inefficiencies. This research paper explores the potential of deep learning approaches, specifically neural network and transfer learning with MobileNetV2, for accurate and efficient rice variety classification. The paper builds upon previous studies that have demonstrated the superiority of deep learning over traditional machine learning methods. Experimental results using a publicly available dataset of 75,000 rice grain images, labeled with five rice varieties, show that both the neural network and MobileNetV2 models achieve high accuracy rates. The neural network model is trained using stochastic gradient descent with 250 epochs, while the MobileNetV2 model leverages pretraining on the ImageNet dataset and is trained with the Adam optimizer for 10 epochs. The comparative analysis reveals that the MobileNetV2 model performs slightly better, benefiting from its ability to extract features from images learned from the large-scale ImageNet dataset. The findings highlight the potential of deep learning methods for rice variety classification, offering improved accuracy and efficiency in rice quality inspection. The choice between the neural network and MobileNetV2 models depends on the specific requirements and priorities of the application, with the neural network offering flexibility and transparency, and MobileNetV2 providing high accuracy through pre-trained feature extraction. © 2023 IEEE.

关键词： Textures

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YOLOv7 and EfficientNet Based program For Detecting and Classifying Fish in Aquaculture Environment 6

YOLOv7 and EfficientNet Based Program For Detecting and Clas...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Syifa, Kayla Adira Susanto, Justin Winata Giuniano, Kevin Praditha Pratama, Galih Dea Hasana, Silviya Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315639

The potential for aquaculture to supply the world's demand for seafood is enormous. Effective fish monitoring and management in aquaculture, on the other hand, continues to be a serious challenge. This report proposed a study to develop a fish detection and classification system. We used YOLOv7 to detect fish and EfficientNet to classify the fish species. We trained and tested the models on augmented images of 9 different species of fish with 1000 images in each class. The proposed approach has a mAP score of 0.994 while the accuracy of the model was 0.9919 on the train set and 0.8139 on the test set. Our result shows that the combination of YOLOv7 and EfficientNet is applicable for efficient fish identification and categorization in the aquaculture environment. © 2023 IEEE.

关键词： Fish

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