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MRI-Based Alzheimer's Disease Multiclass Classification Using Transfer Learning with EfficientNet B0 to B3 9

MRI-Based Alzheimer's Disease Multiclass Classification Usin...

引用

9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Sennellius, Alves Renato Liu, Fanes Suryaningrum, Kristien Margi Yunanda, Rezki Prasetyo, Simeon Yuda School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Alzheimer's Disease (AD) is the most prevalent neurodegenerative disorder leading to dementia, accounting for 60 to 80 percent of its cases. Even though the effect of AD is devastating, there still is no cure for AD until this day. Transfer Learning (TL) technique involves leveraging the knowledge gained from previous training on one task and is used to solve related tasks which often achieved quite promising results compared to CNN. This study aims to implement EfficientNet B0 to B3 for multiclass AD classification. This study utilized 9,900 MRIs from an open source dataset called alzheimer2 dataset that have been split into training, validation, and testing subset. The MRIs go through several preprocessing steps, including target value distribution, image resizing, and pixel normalization. This study trained the models with epochs of 100 and batch size of 64. The EfficientNet B3 model achieved the highest accuracy of 91.759%, precision of 92.378%, recall of 92.413%, and f1-score of 92.335%. This indicates the potential of EfficientNet B3 in accurately classifying AD based on its severity level. © 2024 IEEE.

关键词： Adversarial machine learning

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A Survey On Indonesian Hoax Analyzer and Fake News Detection Using Deep Learning Techniques 6

A Survey On Indonesian Hoax Analyzer and Fake News Detection...

引用

6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Laimeheriwa, Jonathan Iswanto, Irene Anindaputri Oktriani, Wiwi Hidayat, Muhammad Fadlan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

In today's digital world, where people consume information quickly through social media like Facebook, Instagram, and Twitter, there's a growing concern about fake news. It's crucial to know whether information and its sources are reliable, especially as people may disregard real news if it doesn't align with their beliefs. This survey aims to explore the landscape of fake news in the context of Indonesia, focusing on the development and implementation of hoax analyzers and fake news detection using deep learning techniques. Our review shows that deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models like IndoBERT have demonstrated effectiveness in identifying misinformation within Indonesian content, with promising accuracy and efficiency metrics. However, challenges persist, including the need for extensive data, computational resources, and tailored approaches to handle Indonesian language complexities. This survey highlights the importance of advancing these detection systems to help reduce the spread of misinformation and support informed decision-making in Indonesian society. © 2024 IEEE.

关键词： Tweets

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Usability evaluation of Ruangguru online learning mobile application using heuristic method 9th

Usability evaluation of Ruangguru online learning mobile app...

引用

9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Kusuma, Hardi Rue, Fulgencia Shaynalie Rumagit, Reinert Yosua Pratama, Galih Dea Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The digital era has transformed education, making online learning platforms crucial to the teaching process. The Ruangguru app has become one of the e-learning platforms widely used in Indonesia, as evidenced by more than 10 million downloads on the Play Store. This study aims to identify and analyze usability issues in the Ruangguru application using the Heuristic Evaluation method. Three experts assessed the app using Nielsen's ten Usability Heuristics principles. The evaluation revealed that while Ruangguru generally demonstrates acceptable usability, specific areas need improvement, particularly in user control and freedom, recognition rather than recall, and aesthetic and minimalist design. Recommendations include adding undo and redo functions, simplifying the user interface, and ensuring consistency in design elements. By implementing these suggestions, developers can enhance user engagement and satisfaction, further promoting interest in learning and education. © 2024 The Authors.

关键词： Contrastive Learning

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Comparative Study of GPU Performance and Energy Efficiency Across Generational Architectures: A Systematic Literature

Comparative Study of GPU Performance and Energy Efficiency A...

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2024 International Conference on Control and Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Muhammad, Risang Faiz Syahputra, Muhammad Edo School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331511166

In this study, the evolution of GPU architectures across multiple generations is explored, focusing on their computational performance and energy efficiency. Using a systematic literature review and comparative analysis, this study examines key advancements in GPU technology, particularly in AI workloads, ray tracing, gaming, and scientific simulations. The analysis reveals that modern GPUs, such as those based on the Ampere architecture, demonstrate significant improvements in both performance and energy efficiency, with a 30% increase in AI task performance and a 20% reduction in power consumption compared to previous generations. This study also highlights how innovations in specialized cores and optimized memory management have enabled GPUs to excel in domain-specific applications. Additionally, the broader implications of these advancements for other computational fields, including gaming and scientific research, are addressed. By providing a comprehensive view of these developments, this review offers valuable insights into future GPU design trends and the ongoing challenges in balancing high performance with energy efficiency. © 2024 IEEE.

关键词： Memory management

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Evaluating Back Translation and Misspelling Correction Utilization on Indonesian AES 5

Evaluating Back Translation and Misspelling Correction Utili...

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5th International Conference on Artificial Intelligence and Data sciences, AiDAS 2024

作者： Amadea Tanaka, Elvina Christian, Steven Anderies Chowanda, Andry School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331528553

This paper aims to tackle the problem of Automatic Essay Scoring (AES), a method used to predict whether an answer is correct given a question based on the provided guidelines and criteria. AES helps teachers to score student answers based on the guidelines fed into the model, saving time and decreases the human-error rate. We had chosen the UKARA challenge dataset, an Indonesian binary classification dataset consisting of two different questions separated into two datasets A and B. Prior studies utilized a stacking approach with XGBoost and a Neural Network model, achieving F1-scores of 88.4% and 75.9% for problem A and B respectively. The latest study utilizing the UKARA dataset used SBERT sentence embeddings and a Neural Network model, resulting in F1-scores of 89.4% for problem A and 75.7% for problem B. Although the F1-score for problem A was satisfactory, the F1-score for problem B remained relatively low. Therefore, this research aim to improve the performance of problem B. This research also discusses the various data augmentation techniques that can be applied to the dataset, such as back translation and misspelling correction Peter Norvig method. Based on the latest research, we decided to use SBERT sentence embeddings and neural networks for the model. The experiment managed to improve the result for problem B with a maximum F1-score of 77.2% along with 89.7% for problem A. © 2024 IEEE.

关键词： Back-Translation Indonesian AES IndoSBERT Sentence Embed-ding Neural Network

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Implementation of Artificial Intelligence on Air Traffic Control - A Systematic Literature Review 18

Implementation of Artificial Intelligence on Air Traffic Con...

引用

18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Emha Abdillah, Risya Moenaf, Henry Fadullah Rasyid, Luthfi Achmad, Said Sutoyo, Rhio School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350331011

Humans can now do jobs more efficiently, and many problems can be solved with the assistance of Artificial Intelligence (AI). AI also plays an important role in air traffic control because it can help improve the safety and efficiency of air travel. AI can be used for various tasks, such as predicting weather patterns, identifying potential conflicts, and recommending optimal routes. Machine learning can also be applied in air traffic control to help systems learn from data and improve performance. This research aims to show the role of artificial intelligence (AI) in the Air Traffic Control (ATC) system and explore ways to implement AI to improve the system's performance. This study reviewed literature by looking at relevant articles and journals to achieve the result. This research reviews previous literature based on keywords Such as Implementation, Air Traffic Control, and Machine learning. Based on the literature review results, the various aspects of air traffic control and management are increasingly dependent on AI and machine learning. This study found that 7 aspects of ACT have adopted AI and have the potential to continue to develop their use. © 2024 IEEE.

关键词： Air traffic control

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Development of a Secure Web Based Application to Automate Data Synchronization and Processing 9th

Development of a Secure Web Based Application to Automate Da...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Artajaya, Hansen Julieta Giancarlos, Jose Moniaga, Jurike V. Chowanda, Andry Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This study aims to develop a secure web-based application that provides job recommendations for students and allows staff to process company data more efficiently. The application utilizes web scraping on internal websites to automate data processing, reducing the need for manual intervention. The data contains personal information. Therefore, the website should apply filters to the data displayed to students and ensure it is protected from any cyber threats. To ensure the application protected from any cyber threats, The web application is designed with a strong focus on security by using the Web Security Testing Guide (WSTG). The Web Security Testing Guide (WSTG) will guide ensuring that the server runs safely and is protected against vulnerabilities in its code. Furthermore, the researchers utilized the top ten Open Web Application Security Project (OWASP) guidelines as a testing framework for the website, focusing on its most vulnerable areas. © 2024 The Authors.

关键词： Cyber attacks

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Comparative analysis of machine learning decision tree-based models for predicting maternal health risks 9th

Comparative analysis of machine learning decision tree-based...

引用

9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Setiawan, Karli Eka Kurniawan, Afdhal Prasetyo, Simeon Yuda Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The reduction of maternal mortality is part of the steps to realize the actualization of the human right to health, which is stated in the United Nations Sustainable Development Goals (SDG). As our contribution, this research proposes some machine learning decision tree-based models such as Decision Tree (DT), ensemble bagging with DT, AdaBoost with DT, and Random Forest, which may inspire parties who have health authority to implement machine learning approaches into their Internet of Things (IoT) system-based systems for supporting health professionals to predict the risk of pregnancy for mothers. The dataset used was the Maternal Health Risk dataset gathered from Kaggle. The result showed that the best machine learning DT-based model was Random Forest with 50 DT models inside, which achieved the best overall accuracy (82%) and best recall on predicting low-risk and high-risk classes with 85% and 88%, respectively, even though that model achieved 73% recall on predicting medium-risk. © 2024 The Authors.

关键词： Contrastive Learning

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Towards a Preliminary Usability Analysis of an Augmented Reality Photo Booth Application for Gen Z: Insights on Users, System, and Interaction for Couples and Non-Couples 6

Towards a Preliminary Usability Analysis of an Augmented Rea...

引用

6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Catherine, Vincentia Joycelyn, Tiffany Fajar, Muhamad Suri, Puti Andam School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

The objective of this study is to further assess and develop ZenithAR, an Augmented Reality based application by adapting it to two distinct groups of people, consisting of 10 individuals in relationship and 10 individuals currently not in any relationship. Usability was evaluated through a questionnaire delving into key aspects such as USE (User, System, Interaction) aspects. With a sample size of 20 respondents, the analysis uncovered no statistically significant disparities in usability between the two user groups, as determined by the Mann-Whitney U test and t-Test (independent samples). These findings suggest that ZenithAR offers consistent usability, regardless of relationship status. © 2024 IEEE.

关键词： Augmented reality

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Alzheimer's Disease Identification through Brain MRI Images: Leveraging Pretrained CNN Models 10

Alzheimer's Disease Identification through Brain MRI Images:...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Prasetyo, Simeon Yuda Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350365283

Alzheimer's disease (AD) poses a significant global healthcare challenge, impacting an estimated 50 million individuals worldwide. As the leading cause of dementia, AD not only affects individual patients but also burdens families, caregivers, and healthcare systems. Moreover, the economic burden of AD is substantial, with healthcare and caregiving costs reaching hundreds of billions of dollars annually. Early detection of AD is crucial, as it allows for timely intervention and planning for patients and families. Recent advancements in medical imaging and machine learning offer promising avenues for early AD diagnosis. This study employs a methodology involving transfer learning and fine-tuning techniques with pretrained CNN models, including Xception, InceptionV3, VGG16, VGG19, and MobileNet, for AD detection using MRI brain images. The results demonstrate significant accuracy improvements during fine-tuning, with VGG19 achieving a perfect accuracy score of 100.00%. These findings underscore the urgency of developing accurate and efficient diagnostic tools for early AD detection, given the profound societal and economic implications of the disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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