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Addressing the role and opportunities of machine learning utilization in brain tumor detection

Procedia computer science 2024年 245卷 869-878页

作者： Vallerie Delia Lesmana Holly Agustine Irma Kartika Wairooy Brilly Andro Makalew Computer Science Department School of Computer Science - Binus University Jakarta 11480 Indonesia Mobile Application & Technology Program Computer Science Department - Binus University Jakarta 11480 Indonesia

This research aims to develop a brain tumor detection model by utilizing the machine learning techniques and Convolutional Neural Network (CNN). A significant matter to address is revolving around early detection and the proper handling regarding the brain tumor. This research's methodology consists of collecting the dataset, identifying the tools and language to use, prepare and preprocessing the data, data augmentation, splitting and label encoding, building the model architecture, compiling the model, training, and evaluating the model, predicting the model, and comparing it with other models. Dataset consists of 7022 MRI images, divided into training and testing subsets; and four classes: glioma, meningioma, pituitary, and no tumor. There are four different CNN models that have been built and evaluated, namely VGG16, InceptionV3, ResNet50, and DenseNet121. The result gained shows VGG16 with the best performance achieving an accuracy rate of 96.43%, followed by DenseNet121 (94,96%), InceptionV3 (92,40%), and ResNet50 (78,69%). Although there is still room for improvement regarding overfitting and increasing the models’ overall performance, this result is promising enough to enhance early diagnosis and offer an appropriate and effective treatment for patients.

关键词： Machine Learning Deep Learning CNN Brain Tumor Detection MRI VGG16 InceptionV3 ResNet50 DenseNet121

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The future of virtual reality: Prospect and problems

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Procedia computer science 2024年 245卷 355-364页

作者： Andrew Alfonso Lie Owen Tamashi Buntoro Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Virtual Reality (VR) is a technology that allows users to interact with a simulated environment created by virtual reality capabilities through the intermediary of computers, simulator tools, and others, providing new experiences and can feel experiences that are not possible in the real world. This research aims to identify and analyze what challenges may occur such as technical, social, and ethical barriers in the development and application of VR technology. Using a mixed methods approach, we collected quantitative data from Kaggle's dataset and conducted in-depth interviews with active VR users. Our findings show that although VR has significant potential in various fields, such as education, healthcare, and entertainment, challenges such as motion sickness, high costs, and limited and engaging content hinder the widespread adoption of VR technology. The study also highlights the importance of improving technologies that need to be implemented such as visual capabilities, motion responsiveness, and audio quality to enhance the user experience when using VR. By overcoming these barriers, VR can maximize its positive impact and become a revolutionary tool in various sectors.

关键词： Virtual Reality immersive experience technical barriers social impact ethical considerations

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Quantitative analysis of sign language translation using artificial neural network model

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Procedia computer science 2024年 245卷 998-1009页

作者： Fendy Wijaya Leonardo Dahendra Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Sign language is one of the technique to support communication with deaf and speech impaired people. Nowadays, human needs become more complex, so are the needs of people with those disabilities. Therefore, with the sophistication of modern technology in the field of computer science, it is necessary to have a sign language translation system which is capable to convert human gestures into words or spelling letters in a natural human language, especially Indonesian language and SIBI sign language. This research is conducted to analyze the capabilities of an Artificial Neural Network (ANN) system to translate SIBI sign language into Indonesian language. The analysis is performed quantitatively from the experiment results to gain a descriptive insights for the generated models. By creating two datasets containing alphabets and words in SIBI sign language, two models were generated to predict alphabets and words, respectively. The result shows that ANN model could effectively and efficiently perform a sign language translation with accuracy of alphabet prediction reached 96.15% and words prediction reached 99.45% in duration of prediction without exceeding 0.15 seconds. Regarding to the results, it can be concluded that ANN model quantitatively suitable to be implemented as an Indonesian sign language translator system.

关键词： sign language Artificial Neural Network SIBI prediction quantitative analysis

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Harnessing Deep Learning for Ocular Disease Diagnosis

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Procedia computer science 2024年 245卷 914-923页

作者： Jessica Ryan Dave Andrew Nathaniel Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Vision impairment, often caused by preventable ocular diseases can be challenging to diagnose accurately and prone to human error. Automation using technology, particularly deep learning, offers a promising solution to aid in accurate and efficient disease detection. This study explores the use of different CNN models specifically VGG-16, VGG-19, ResNet-50, and ResNet-152v2, for detecting ocular diseases. Simple fine-tuning is applied to these models, and their performance is compared to identify the most effective model. The purpose is to show how different models contribute to establishing reliable illness detection systems. The results reveal that most of these models perform well with even minimal fine-tuning. Among the models, ResNet-152v2 achieved the highest training accuracy of 90.36% demonstrating its capacity to learn from the training data. In contrast, ResNet-50 offered a more balanced performance with marginally lower accuracy, making it a robust choice for general application.

关键词： ocular diseases deep learning classification

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Trend analysis in competitive programming using topic modeling

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Procedia computer science 2024年 245卷 591-600页

作者： Albert Yulius Ramahalim Christoffer Edbert Karuniawan Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Competitive programming (CP) is a mind sports activity where people solve problems using command-line computer programs to provide correct output for the given test cases. Competitors need to practice problem-solving and mathematics as well as study algorithms and data structures to perform well in CP. This study aims to provide an original way to perform a trend analysis in CP, distinguishing topics frequently used in CP contests. To fulfill our goal, we create topic models based on previous topic modeling works to do natural language processing tasks using Latent Dirichlet Allocation (LDA) and Biterm Topic Model (BTM). For our dataset, we constructed a corpus from Codeforces blog posts, a popular website for competitive programmers, by extracting its content and user comments. Our results indicate that BTM is powerful enough to do trend analysis in CP. The trend analysis recognized that dynamic programming and complexity analysis have been the most prominent topics for the last ten years. Data structures and string algorithms are runners-up that may have potential trends in the future. This study opens up further research on other methods to perform trend analysis using better topic models and corpora.

关键词： competitive programming topic modeling trend analysis

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Mental illness detection using sentiment analysis in social media

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Procedia computer science 2024年 245卷 971-978页

作者： Kasimirus Derryl Odja Jasson Widiarta Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

This research tries to detect mental illness using sentiment analysis on Reddit data, as well as comparing the performance of the k-Nearest Neighbors (k-NN), Random Forest, and Neural Network models. Using text post data from the pre-pandemic and post-pandemic periods, we concluded that the Random Forest model had the highest overall performance with an F1 Score, accuracy, recall and precision of 80.6%, making it quite effective in detecting depression. Even though the Neural Network model shows slightly lower accuracy, namely 79%, in fact this model has the lowest error rate, namely 0.06496. The k-NN model showed the lowest accuracy and higher error rate. These findings highlight the potential of sentiment analysis and machine learning in identifying mental health issues on social media and suggest that better models can improve early detection and intervention efforts.

关键词： Sentiment Analysis Mental Illness k-NN Random Forest Neural Network

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Enhancing Waste Classification with YOLOv8 Models for Efficient and Accurate Sorting

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Procedia computer science 2024年 245卷 889-895页

作者： Deverel Vieri Rendy Susanto Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

As of 2023, Indonesia ranks as the second largest waste-producing country globally. The waste produced is not segregated properly, leading to the vast amount of waste piling up in local landfills. Traditional methods, such as manual sorting, have been widely used to segregate waste but suffer from inefficiencies and inaccuracies. In contrast, deep learning models offer an alternative solution for waste classification, overcoming the limitations of traditional methods. A deep learning approach using YOLOv8 was proposed to classify waste into six distinct categories. Three different YOLOv8 variants: nano, small, and medium, are trained after the dataset has been augmented into 3,500 labeled images. The results indicate that these models were able to achieve high accuracy in classifying images, with the nano variant having the least training time and an accuracy of approximately 89%.

关键词： Waste Classification Waste Management Artificial Intelligence Machine Learning Deep Learning Convolution Neural Network YOLOv8

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Design and Implementation of Embedded Qinghai Tourism Customer Service Question-and-Answer Robot Based on ChatGPT 24

Design and Implementation of Embedded Qinghai Tourism Custom...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Wu, Jing Guo, Zhenxin Wang, Zhicheng Zhang, Haotian Kang, Xaolin Ma, Xiaoguang Department of Computer and Information Science Qinghai Institute of Technology China Foshan Graduate School Northeastern University China School of Computer Science and Application Qinghai University China

ISBN: (纸本)9798400718137

The application of big data technology has brought new impetus and possibilities to the development of tourism. The design of an embedded Q&A robot system for Qinghai tourism customer service based on ChatGPT system aims to provide an intelligent customer service solution to help users obtain information about Qinghai tourism and answer questions. The system uses ChatGPT as the core dialogue model, combines the knowledge and data in the field of Qinghai tourism, and realizes the intelligent answer to user questions. This study application in the field of natural language processing, and analyzes the design requirements and implementation scheme of embedded travel question answering system in detail. The function module includes user login and registration, travel tickets, hotel information query, scenic spot ticket price query and other essential basic function modules of the tourism website. Whether the design is reasonable or not will also directly affect the user experience. The results show that ChatGPT embedded in Qinghai tourism Q&A system can not only provide more intelligent and accurate answers, but also enhance the interactivity and practicability of the system, and provide users with more convenient and personalized tourism information services. © 2024 Copyright held by the owner/author(s).

关键词： Sales

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Extracting Aspect Sentiment from Users' Book Reviews for Recommendation

Extracting Aspect Sentiment from Users' Book Reviews for Rec...

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2024 International Conference on Computational Linguistics and Natural Language Processing, CLNLP 2024

作者： Cui, Yachao Zhou, Peng Li, Kang Seng, Bingfeng Cao, Han Shaanxi Normal University School of Computer Science Xi'an China Qinghai University Department of Computer Technology and Application Xining China

ISBN: (纸本)9798350392098

When shopping for books on e-commerce platforms, other users' evaluations of books contain a large number of users' intuitive feelings, from which useful information can be mined to provide buyers with ideas for shopping. In terms of recommendation algorithms, although the existing research work can use various methods to model fine-grained user preferences to improve the recommendation effect. However, we believe there are still shortcomings. First, existing methods are overly dependent on the accuracy of external sentiment analysis tools and are not necessarily applicable to the recommendation domain. Second, the features extracted from reviews are still potential features when incorporated into recommendation algorithms, and they cannot be fully utilized to characterize user embeddings. For this reason, this paper proposes a new user comment extraction scheme for personalized recommendation. We first use text summarization techniques in the field of NLP to preprocess and refine the comment text to reduce data noise and obtain high-quality text information. Subsequently, in order to more accurately characterize user embeddings and item features, we use advanced aspect-based sentiment analysis techniques to extract fine-grained aspectual items and corresponding sentiment polarity from the review text. Finally, experimental validation on a real book dataset is conducted to demonstrate the effectiveness of our model. © 2024 IEEE.

关键词： Embeddings

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Database Availability Security: Handling Denial of Service (DoS) Attacks

Database Availability Security: Handling Denial of Service (...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Jeffrey Surianto Irma Kartika Wairooy Sandy Wirananda Brilly Andro Makalew Computer Science Department School of Computer Science Jakarta Indonesia Computer Science Department Mobile Application & Technology Program Jakarta Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

In the data based digital age, there are risks of attacks that cause denial of service (DoS) or distributed denial of service (DDoS) which pose threats to the security and availability of databases especially with those who are affected by this which is very disturbing. This research aims at analysing the methods of detecting and preventing DoS and DDoS attacks, with more focus on the security of databases. This research identifies and evaluates various strategies employed to counter such threats to cloud and Internet of Things (IoT) systems from the literature review conducted using the PRISMA approach. The analyses show that machine learning algorithms are capable of enhancing the detection of DoS/DDoS attack's efficiency and accuracy, and the cloud and IoT security solutions, including complex load balancing and powerful firewalls, are crucial for preventing and mitigating such attacks. This study also shows that, AI should be included in the security plans and security measures should be adaptable and proactive. This work is useful for scholars, developers, and cybersecurity experts who are interested in enhancing measures to counter and prevent advanced cybercrimes and ensuring continued and reliable functionality in data centric settings.

关键词： Firewalls (computing) Prevention and mitigation Distributed databases Telecommunication traffic Denial-of-service attack Load management Security Internet of Things computer crime Protection

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