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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Rahajeng, Salicca Dewi Nurfitriyani, Siti Julianingsih Akbar, Zakin Izzani Leonardo, Calvin Warnars, Harco Leslie Hendric Spits Warnars, Laurens Spits Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350354348

As social beings, humans like to interact with each other, including other creatures of God, such as animals, and keep them as pets. Many pets, such as dogs, cats, birds, fish, rabbits, and so on, include unusual pets such as reptiles, pigs, insects, etc. As a result of the lack of human awareness of pets and the nature of keeping animals, which is only for fun, it is not soulful to keep animals. So when people are bored, they abandon their pets, which causes new problems in human life today. In this case, we created a mobile application that can be shared with pet lovers where they can share information about abandoned pets and the possibility of owning pets from these abandoned pets. Mobile applications were designed using use case diagrams to show business processes, class diagrams to show database models, and user interfaces to present a similar interface to users. This mobile application will become a platform that can gather all people who care about pets and make pets a part of humans. © 2024 IEEE.

关键词： Mobile applications

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Towards Federated Learning on Fresh datasets 20

Towards Federated Learning on Fresh Datasets

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20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Wu, Chen Xiao, Mingjun Wu, Jie Xu, Yin Zhou, Jinrui Sun, He University of Science and Technology of China School of Data Science China University of Science and Technology of China School of Computer Science and Technology & Suzhou Institute for Advanced Research China Temple University Department of Computer and Information Sciences United States

ISBN: (纸本)9798350324334

Federated Learning (FL) is an emerging privacy-preserving distributed computing paradigm that enables numerous clients to collaboratively train machine learning models without the need for transmitting the private datasets of clients to the FL server. Unlike most existing research where the local datasets of clients are considered to be unchanging over time during the whole FL process, we consider such scenarios in this paper where the local datasets of clients need to be updated periodically, and the server can stimulate clients to use as fresh as possible datasets to train their local models. Our objective is to determine a client selection strategy to minimize the loss of global model for FL with a limited budget. To this end, we leverage the concept of Age of Information (AoI) to quantify the freshness of local datasets and theoretically analyze the convergence bound of our AoI-aware FL system. Based on the convergence bound, we formalize our problem as a restless multi-armed bandit problem. Then, we devise a Whittle's-Index-based Client Selection algorithm, called WICS, to tackle the client selection problem. Extensive simulations show that the proposed algorithm can reduce the training loss and improve learning accuracy compared to state-of-the-art algorithms. © 2023 IEEE.

关键词： Budget control

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HybridViT: An Approach for Alzheimer’s Disease Classification with ADNI Neuroimaging data

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SN computer science 2025年第4期6卷 1-18页

作者： Tekumudi, Vivek Sai Surya Chaitanya Ramanathan, Annamalai Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

Alzheimer’s disease (AD) is a prevalent neurological disorder characterized by progressive brain cell degeneration and atrophy, leading to a gradual decline in cognitive and functional abilities. Timely diagnosis is paramount in potentially delaying or preventing the course of this debilitating condition. Magnetic Resonance Imaging presents a non-invasive means for longitudinal monitoring and serves as a crucial biomarker for tracking disease progression. In particular, Structural Magnetic Resonance Imaging (sMRI) enables the quantification of atrophy, which is a reliable indicator for assessing the precise stage and severity of AD-related neurodegeneration. In this research, we employ advanced machine learning techniques to address the challenge of accurate and early diagnosis of AD. We utilize a comprehensive dataset comprising five stages of 2D sMRI Image data, encompassing AD, Cognitively Normal, Mild Cognitive Impairment, Early Mild Cognitive Impairment, and Late Mild Cognitive Impairment classes. To optimize the classification process, we explore a novel approach that combines a Vision Transformer with pre-trained convolutional neural networks. Our study includes binary and multi-class classification tasks, and the performance of 26 pre-trained Keras Deep Learning models is assessed. Notably, The DenseNet121+ViT model achieves 91.26% accuracy in multi-class classification. For binary classification, MobileNetV2+ViT, EfficientNetB4+ViT, and MobileNet+ViT achieve accuracies of 92.33%, 97.82%, and 94.81%, respectively. These results highlight our approach’s potential to improve AD diagnosis accuracy and underscore the importance of deep learning in early detection of neurological disorders. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Alzheimer’s disease (AD) Alzheimer’s disease neuroimaging initiative (ADNI) HybridViT Pre-trained models Vision transformer

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Jakarta Flood Map: Map Visualization for Flood Area in Jakarta Using Spatio-Temporal Analysis

Jakarta Flood Map: Map Visualization for Flood Area in Jakar...

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2023 International Conference on Networking, Electrical Engineering, computer science, and Technology, IConNECT 2023

作者： Wijaya, Annabelle Sharon Wijaya, I Nyoman Wahyu Wijaya, Stefanova Anderson, Keane Deovan Kurniadi, Felix Indra Riccosan Pramudya, Fabian Surya Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350331172

Floods are a frequent occurrence in Indonesia, particularly in DKI Jakarta. Flood disasters cause significant losses to human health, the economy, and environmental damage. This research aims to assist government data collection efforts and raise public awareness through Map Visualization of flood-prone areas in Jakarta using ArcGIS. The data processing begins with data collection and data cleaning methods applied to a dataset encompassing flood quantities from 2021 to 2022. The processed dataset is then used for modeling in ArcGIS to generate map visualizations and analyze them with the spatiotemporal analysis method. The research results are expected to effectively inform the government and community to collaborate in future flood prevention initiatives in Jakarta. © 2023 IEEE.

关键词： Floods

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The Comparison of Random Forest and Artificial Neural Network for Customer Churn Prediction in Telecommunication 3

The Comparison of Random Forest and Artificial Neural Networ...

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3rd International Conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2023

作者： Ramadhan, Aldandi Fitra Permai, Syarifah Diana Harefa, Jeklin Alexander Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781509062805

Customer churn is a significant issue that can affect a company's revenue, particularly in the telecommunication sector. Companies today are working hard to survive in this competitive market by implementing various types of strategies. The churn prediction system can be used as a strategy for long-term business success or failure. In this research, variable reduction was carried out using the chi-square test and RFE to select several influential features to be used as input in the model. The implementation of the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC) is also carried out to handle the imbalanced data. This research compares the Random Forest and Artificial Neural Network algorithms to obtain the best accuracy in predicting telecommunication customer churn. In addition, Grid Search is used to perform parameter tuning to obtain the best parameters from each method. Therefore, this research obtained 4 models that were compared. From this experiment, it can be inferred that the Random Forest model with some hyperparameter tuning works best with an AUC (Area Under Curve) value of 75% compared to the Artificial Neural Network with an AUC value of 65%. © 2023 IEEE.

关键词： Neural networks

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Development of 3D Scanner Application with Stereo Camera for 3D Object Reconstruction 8

Development of 3D Scanner Application with Stereo Camera for...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Lukita, Ridan Pranoto, Hady Faisal Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Visual perception in RGBD (Red-Green-Blue and Depth) camera technology allows the camera to provide color image and depth structure information simultaneously from the surface of the scanned object. Based on this technology, data from an RGBD camera can be processed to reconstruct the scanned object in a three-dimensional (3D) model. However, devices that are prebuilt to perform this task with good accuracy are expensive and are only intended for 3D scanning tasks. On the other hand, some multi-purpose depth cameras can be used for 3D scanning but require some tweaks to ensure a good and accurate reconstructed model. A method has been proposed to optimize the camera and reconstruction system to compile RGBD information from recorded close-up objects and process them into a full-scale 3D digital model. This process involves developing the appropriate recording method along with settings and adjustments for scanning and model reconstruction of close-range objects. As a result, information from multi-purpose RGBD camera can be reconstructed in three-dimensional form that accurately represents real-life objects. The reconstructed model can be utilized for applications that rely on real-life objects as their main basis. The proposed method enables the use of inexpensive multi-purpose RGBD cameras for 3D scanning. © 2023 The Authors. Published by Elsevier B.V.

关键词： 3D scan reconstruction RGBD

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HealthBridge: A Decentralized and Interoperable Healthcare App for Rare Disease Communities

HealthBridge: A Decentralized and Interoperable Healthcare A...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Ziyuan Wang, Yiwei Liang, Anda Van Devanter, Wyn Zhang, Peng Vanderbilt University Department of Computer Science NashvilleTN United States Institute Vanderbilt University Department of Computer Science and Data Science NashvilleTN United States

ISBN: (纸本)9798350337488

Traditional centralized database systems share many weaknesses, including the overhead of sharing medical documentation, data fragmentation, and susceptibility to centralized data attacks. Those problems would especially influence rare disease patient communities given their higher need for medical data interoperability and security. In this paper, we present an alternative approach to managing healthcare data that is tailored to the rare disease community. Specifically, we design and implement HealthBridge, a decentralized and interoperable mobile application with distributed ledger technology. The underlying permissioned network utilizes Hyperledger Fabric and has a modular design, empowering efficient data transactions directly between trusted entities with predefined smart contracts that regulate access permissions. For the application design, we focus on the needs of the rare disease community, prioritizing ease of use without compromising data security. In particular, our application provides a delegate feature, aimed at supporting rare disease patients with additional needs. Through this feature, if needed, patients can grant pre-defined access permissions to delegates through smart contracts. While the overall implementation of HealthBridge is still in the developmental stages, it presents a proof of concept and a promising system to support the underfunded rare disease community in terms of healthcare interoperability through a permissioned blockchain network. © 2023 IEEE.

关键词： healthcare informatics interoperability mobile application permissioned blockchain rare disease

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Hotel Information System Management Audit Using COBIT 2019 - APO12 2

Hotel Information System Management Audit Using COBIT 2019 -...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Utamajaya, Joy Nashar Supangat, Suhono Harso Gaol, Ford Lumban Ranti, Benny BINUS University BINUS Graduate Program School of Information System Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatic Bandung Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia University of Indonesia Computer Science Department Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350320923

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company's business goals. Given the large contribution of information technology in the application of hotel applications as a supporting information system, it is a vital system that must avoid risks that can hinder and cause harm to the hotel management business processes. Therefore, it is necessary to carry out risk management using the COBIT 5 framework to manage possible risks that may occur based on the APO12 (Manage Risk) domain. From the evaluation results of the data obtained through observation and interviews as well as the calculation of the results of the questionnaire based on 6 APO12 subdomains, the results of the risk management level capability assessment in hotel applications are still at level 3 or have reached the level of established process with the target to be achieved at level 4 resulting in a gap of 1 level. Based on the results obtained, it is necessary to propose recommendations that can be used by hotels in improving the application of information technology risk management so that in the future it can achieve the expected target level so that the APO12 level of capability can increase and become more optimal. © 2023 IEEE.

关键词： Risk management

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Effect of pulsed current cathodic protection on pipeline steel API 5L X65 corrosion mitigation: An investigation and machine learning-assisted modeling

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Corrosion Communications 2023年第4期12卷 29-45页

作者： Hosein Eslamian Mehdi Javidi Mohammad Reza Zamani Mohammad Mahdi Dana Eghbal Mansoori Department of Materials Science and Engineering School of Engineering Shiraz University School of Metallurgy and Materials Engineering College of Engineering University of Tehran Computer Science Eng.and IT Dept. School of Electrical and Computer Engineering Shiraz University

Pulsed current cathodic protection(PCCP) could be more effective than direct current cathodic protection(DCCP)for mitigating corrosion in buried structures in the oil and gas industries if appropriate pulsed parameters are chosen. The purpose of this research is to present the corrosion prevention mechanism of the PCCP technique by taking into account the effects of duty cycle as well as frequency, modeling the relationships between pulse parameters(frequency and duty cycle) and system outputs(corrosion rate, protective current and pipe-to-soil potential) and finally identifying the most effective protection conditions over a wide range of frequency(2–10 kHz) and duty cycle(25%-75%). For this, pipe-to-soil potential, pH, current and power consumption, corrosion rate, surface deposits and investigation of pitting corrosion were taken into account. To model the input-output relationship in the PCCP method, a data-driven machine learning approach was used by training an artificial neural network(ANN). The results revealed that the PCCP system could yield the best protection conditions at 10 kHz frequency and 50% duty cycle, resulting in the longest protection length with the lowest corrosion rate at a consumption current 0.3 time that of the DCCP method. In the frequency range of 6–10 kHz and duty cycles of 50%-75%, SEM images indicated a uniform distribution of calcite deposits and no pits on cathode surface.

关键词： Pulsed current cathodic protection Duty cycle Frequency Artificial neural network API X65 pipeline steel

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A Systematic Literature Review: Forecasting Stock Price Using Machine Learning Approach

A Systematic Literature Review: Forecasting Stock Price Usin...

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2023 International Conference on data science and Its Applications, ICoDSA 2023

作者： Lumoring, Niclauss Chandra, Daniel Gunawan, Alexander Agung Santoso Bina Nusantara University School of Computer Science Mathematic Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350305197

With the increasing popularity of stock trading, individuals, and financial entities such as investment companies, hedge funds, and retail investors are actively participating in the stock market to generate profits. Numerous strategies, ranging from traditional methods employing fundamental and technical analysis to modern approaches utilizing cutting-edge technology, have been developed and implemented. However, determining the optimal method remains challenging. Designing an effective strategy in the complex and dynamic stock market environment poses significant difficulties. Therefore, this study aims to provide an overview of machine learning applications in the stock market and identify the most frequently used machine learning models or methods for market prediction. Furthermore, the study aims to identify the strategy that achieves the highest accuracy in predicting stock prices. Based on a systematic literature review encompassing quantitative and qualitative analyses, it is found that Support Vector Machine (SVM) emerges as the most popular machine learning technique for predicting stock prices. However, Long Short-Term Memory (LSTM) stands out as the machine learning technique that demonstrates the highest level of accuracy, achieving an impressive accuracy rate of 99.58%. These findings highlight the effectiveness of LSTM in forecasting stock values with exceptional precision. The results of this study contribute to a comprehensive understanding of machine learning in the stock market domain and offer valuable insights for developing effective trading strategies. Traders and investors can leverage LSTM and SVM techniques to enhance their stock market predictions and make informed investment decisions. © 2023 IEEE.

关键词： Financial markets

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