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Optimized LightGCN for Music Recommendation Satisfaction 6

Optimized LightGCN for Music Recommendation Satisfaction

引用

6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Hansel, Aaron Christian Adrianus Pradana, Lunardi Suganda, Abba Girsang Nugroho, Ariadi Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

With the large amount of information available on the internet, recommendation tasks have grown to be more crucial than ever. Businesses that store digital media on the internet such as video streaming and music streaming platforms, benefit a lot from recommendation systems. A simple yet powerful recommendation system that can give better recommendation performance is always being sought after. Light Graph Convolution Network (LightGCN) is a simplified version of Graph Convolution Network (GCN) for collaborative filtering in recommendation systems. LightGCN architecture includes only the most essential part of GCN for collaborative filtering that is the neighborhood aggregation, it removes the feature transformation and nonlinear activation because both of them contribute little to no effect to the recommendations. The focus of this research is to optimize LightGCN by tuning the hyperparameters using exhaustive search (grid search). The optimized LightGCN model is able to out-perform LightGCN by more than 140% in music recommendation © 2022 IEEE.

关键词： exhaustive search hyperparameter tuning LightGCN music recommendation Recommendation system

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AI-Based Video Analysis for Driver Fatigue Detection: A Literature Review on Underlying Datasets, Labelling, and Alertness Level Classification 1

引用

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

作者： Ariansyah, Dedy Rahutomo, Reza Elwirehardja, Gregorius Natanael Asadi, Faisal Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Reduced alertness because of fatigue or drowsiness accounts for a major cause of road accidents globally. To minimize the likelihood of alertness reduction-related crashes, a video-based detection emerges as a non-intrusive method and can offer high accuracy for the countermeasure development. Many Artificial Intelligence (AI) techniques have been developed to aid in video analysis for fatigue detection. However, the issues in AI-based video analysis for fatigue countermeasure development are rarely discussed. This paper reviews current video-based countermeasure development in the literature and highlights some reliability issues of AI-based fatigue countermeasure development in terms of underlying dataset, labelling method, and alertness level classification. The result serves as insights and consideration to academics and industrialists in developing a high reliable AI-based video analysis for driver fatigue countermeasure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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A Systematic Literature Review of CNN Approaches in Classifying Brain Tumor 6

A Systematic Literature Review of CNN Approaches in Classify...

引用

6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Paulindino, Aedentrisa Yasmanda Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350345162

The ability of Convolutional Neural Networks (CNNs) to accurately discriminate between normal and tumorous brain tissues has been promising. The review focuses on the different CNN models, pre-processing methods, data augmentation, and Transfer Learning (TL) strategies used in this research. This Systematic Literature Review (SLR) collected the data from Google Scholar. The results of this study indicate that open-source datasets from Kaggle and Brain MRI Images for Brain Tumor Detection are the most used datasets. However, limited data and imbalanced class problems remain common challenges across various datasets. To overcome those challenges, using a larger dataset, oversampling, Generative Adversarial Network (GAN), federated learning, and Self-Supervised Learning (SSL) to handle the imbalance are the potential solution. Additionally, popular CNN architectures for brain tumor classification extensively use pre-trained models such as VGG16, VGG19, DenseNet121, DenseNet201, GoogleNet, ResNet-50, and Inception-v3. TL strategies are preferred, allowing CNNs to leverage knowledge from large datasets, improving generalization even with limited labeled data. © 2023 IEEE.

关键词： brain tumor classification cnn mri pre-trained transfer learning

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Implementation of Dynamic Image for Facial Expression Recognition on Indonesian Facial Expression Dataset 6

Implementation of Dynamic Image for Facial Expression Recogn...

引用

6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Iswanto, Irene Anindaputri Chowanda, Andry Soeparno, Haryono Budiharto, Widodo Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350389692

In recent times, there has been considerable attention directed towards Facial Expression Recognition (FER) due to its extensive utility across diverse domains. However, the universality of facial expressions has been challenged by studies suggesting that cultural backgrounds significantly influence the perception and recognition of emotions. This paper addresses the need for culturally specific datasets in FER tasks, particularly in underrepresented regions like Indonesia. The study introduces dynamic images as an alternative input representation for facial expression recognition tasks, aiming to assess their efficacy using the Indonesian Mixed Emotions Dataset (IMED). Through experimentation using EfficientNet model, the performance of dynamic images is compared with static image and video inputs. Results indicate that dynamic images exhibit promising performance, with an accuracy of 94.28%. These results outperform static image datasets and nearly match the performance of video-based models, which achieved an accuracy of 97.93 %, despite using fewer data. Nonetheless, challenges such as data imbalance and the quality of generated dynamic images persist, suggesting avenues for further research and model refinement. This study provides valuable insights into methodological advancements in FER, particularly in limited dataset conditions, laying the groundwork for future developments in dynamic image-based facial expression recognition algorithms. © 2024 IEEE.

关键词： Dynamic Image EfficientNet Facial Expression Recognition IMED

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Pap Smear Medical Image Classification Using Deep Learning: A Systematic Review 6

Pap Smear Medical Image Classification Using Deep Learning: ...

引用

6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Selvano, Elvan Elwiehardja, Gregorius Natanael Pardamean, Bens Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia

ISBN: (纸本)9798350345162

Cervical cancer has been known as one of the most prevalent medical disorders globally and a leading cause of death. Early detection, particularly through Pap tests, plays a vital role in its prevention. Previous studies have leveraged machine learning and deep learning techniques to classify the medical images obtained from Pap tests. In this study, a Systematic Literature Review methodology was used to examine 15 relevant papers that have been filtered from queries to Google Scholar which have gone through 4 stages of filtering that include: identification, screening, eligibility, and inclusion. This study addresses two research questions regarding the datasets and deep learning techniques for classifying pap smear images in recent years. The performance of the models was analyzed and potential areas for improvements are suggested. The findings of this study reveal that the Herlev University Hospital and SIPaKMed datasets are the most used. The methodologies used by researchers range from machine learning techniques, transfer learning using Convolutional Neural Networks, and utilize state-of-the-art models with novel optimizing methodology. While there are exciting opportunities in the field, challenges include model generalization and interpretability. © 2023 IEEE.

关键词： cervical cancer deep learning machine learning pap test

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The Role of Industry 4.0 Technology in Enhancing Small Medium-Sized Enterprises Stability and Growth 15

The Role of Industry 4.0 Technology in Enhancing Small Mediu...

引用

15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Aprilinda, Yuthsi Warnars, Harco Leslie Hendric Spits Meyliana, Meyliana Prabowo, Harjanto Hidayanto, Achmad Nizar Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Departement Jakarta Indonesia

ISBN: (纸本)9798350383829

The purpose of this study is to find out and analyze what has been done by previous studies in knowing the problems faced by SMEs today and how the influence of Industry 4.0 technology in dealing with problems in Small and Medium Enterprises (SMEs), so that they become developed and can maintain their business continuity. Most studies show that SMEs have considerable barriers when trying to use the potential of Industry 4.0 to improve the competitiveness and long-term sustain ability of their business. This systematic literature review uses the Kitchenham technique to identify SME problems and the role of Industry 4.0 technologies in SMEs in various industrialized and developing countries. Through several databases including: IEEE, science Direct, Web of science, Taylor & Francis, Emerald and Springer between 2019 - 2023 were used to find relevant research articles in this study. The research findings offer valuable perspectives on the strategies, obstacles and prospects that SMEs in different countries face in adjusting to Industry 4.0. The contribution of academics in this research is to provide solutions by designing more robust approaches to promote the business sustainability of SMEs and their role in driving economic progress in their countries. © 2023 IEEE.

关键词： Sustainable development

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Comparison of Spelling Error Correction Algorithms for the Indonesian Language 11

Comparison of Spelling Error Correction Algorithms for the I...

引用

11th International Conference on Information and Education Technology, ICIET 2023

作者： Yanfi, Yanfi Setiawan, Reina Soeparno, Haryono Budiharto, Widodo Bina Nusantara University BINUS Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9781665465489

Edit distance as a string measurement metric is often used to help detect misspellings in languages. This paper aims to compare two string spelling error correction algorithms for the Indonesian language. The N-gram, Jaro-Winkler distance, and Levenshtein distance algorithms are used to determine whether they can accurately correct typological errors in the Indonesian language. Moreover, this study utilized KNIME tools to process the data from beginning to end. The data was retrieved from news in Indonesia. After the experiment on N from 1 to 12, the results obtained for the comparative analysis proved that Jaro-Winkler distance performed better than Levenshtein distance for comparing smaller strings like words and names. However, Levenshtein distance performs as well as Jaro-Winkler distance started from four strings. Finally, both Jaro-Winkler distance and Levenshtein distance algorithm got the best performance accuracy for eight strings with an accuracy of 99.52 percent. The result of this study is also presented that both algorithms can support word error correction for the Indonesian language. © 2023 IEEE.

关键词： Error correction

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GAN.s Approach On Generating Synthetical Skin Image Dataset for Imbalance Dataset 8

GAN.s Approach On Generating Synthetical Skin Image Dataset ...

引用

8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024

作者： Setiawan, Tri Agus Heryadi, Yaya Lukas Wulandari, Lili Ayu Sonata, Ilvico Binus Graduate Program -Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia

ISBN: (纸本)9798350368970

The use of balanced medical datasets is essential to improve the precision and accuracy of machine learning models in the healthcare field. However, dataset imbalance often becomes an obstacle, especially in the diagnosis of skin diseases. This research aims to develop a Generative Adversarial Networks (GAN) method that is more effective in generating synthetic skin datasets to overcome problems in integrating medical datasets. The methods used include developing and training a GAN model to produce realistic synthetic skin images, with a focus on improving the quality and diversity of the synthetic data produced. Important results from this research show that the developed GAN model is able to produce synthetic skin images that are not only realistic but also able to balance the original dataset. The implications of these findings include improving the accuracy and performance of machine learning models in the diagnosis of skin diseases, as well as the potential use of these methods in other medical fields that face similar problems with imbalanced datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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Systematic Literature Review of Transformer Model Implementations in Detecting Depression 6

Systematic Literature Review of Transformer Model Implementa...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Nanggala, Kenjovan Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350345162

This systematic literature review explores the application of transformer models in early detection of human depression, encompassing text, audio, and video data modalities. Transformer architectures, notably BERT for text, have proven adept at capturing crucial contextual and linguistic patterns associated with depression. For audio and video data, hybrid approaches that combine transformer models with other architectures are prevalent. Key features considered include eye gaze, head pose, facial muscle movements, and audio characteristics such as MFCC and Log-mel Spectrogram, along with text embeddings. Performance comparisons underscore the superiority of text-based data in consistently delivering the most promising results, followed by audio and video modalities when utilizing transformer models. The fusion of multiple modalities emerges as an effective strategy for enhancing predictive accuracy, with the amalgamation of audio, video, and text data yielding the most precise outcomes. However, it is noteworthy that unimodal approaches also exhibit potential, with text data exhibiting superior performance over audio and video data. Nevertheless, several challenges persist in this research domain, including imbalanced datasets, the limited availability of comprehensive and diverse samples, and the inherent complexities in interpreting visual cues. Addressing these challenges remains imperative for the continued advancement of depression detection using transformer-based models across various modalities. © 2023 IEEE.

关键词： deep learning major depressive disorder multimodal transformer unimodal

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Service Oriented Fuzzy Decision Model for Road Traffic Engineering 9

Service Oriented Fuzzy Decision Model for Road Traffic Engin...

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

作者： Utama, Ditdit Nugeraha Zaki, Fauzan Affan Munjeri, Intan Juliani Putri, Nidya Utami Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia UIN Syarif Hidayatullah Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350370126

A number of attempts have already been implemented formally to solve road traffic congestion. However, the objective strategy type of road traffic engineering could not be proven truly. Try and error is one inefficient way in road traffic engineering to degrade the level of congestion. The study was conducted to propose a new service-oriented model (SOM) that combines fuzzy-logic and water flow algorithm methods (called FWFA). The method combination was operated as the main method to construct the decision model for selecting the objective strategy in road traffic engineering. Also, service-oriented architecture (SOA) is realized to implement such a constructed model practically. The Model can suggest the most optimal strategy decision in road traffic engineering. Here, a main traffic road of Juanda in area Ciputat, Tangerang Selatan, province Banten, Indonesia;was selected as a research object in this study. The constructed SOM for road traffic engineering was structurally delivered in this paper. © 2023 IEEE.

关键词： Fuzzy logic

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