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The Impact of Information System and Technology of Courier Service During Pandemic 7th

The Impact of Information System and Technology of Courier S...

引用

7th International Conference on Inventive Communication and Computaional Technologies, ICICCT 2023

作者： Siahaan, Andre Timiko Pinajung, Bagas Rizkyka Kennidy Gaol, Ford Lumban Matsuo, Tokuro School of Information System Bina Nusantara University Alam Sutra Tangerang13960 Indonesia Computer Science Department BINUS Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789819951659

Courier services are a means of transport that can be used to deliver orders to customers or claim orders from them, and customers can use technology from the courier service like track order to track their order. The use of courier service nowadays is common that every day, people use courier service as their daily means of transport for delivering goods. However, in situations like pandemic, the courier services need to put more effort into their services, so that customers can use courier services with trust. The courier service must be more careful to deliver customer orders or send orders to customers. For this research methodology, the data collection technique using Google Forms and collecting data directly from people about their opinion toward the research have been employed. The result of the data collecting technique is depicted as the usage of the courier service during COVID-19 pandemic starting from March 2020. The results vary every month because not everyone uses the courier service every day. Moreover, the conclusion on why the months starting from March to June do not show high usage of courier service is that the pandemic has not been that worse than the later months, and so, people were going out to deliver or to do activities like shopping or buying groceries on their own. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Information systems

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

引用

2023 International Conference on computer Science, Information Technology and Engineering, ICCoSITE 2023

作者： Hidayaturrahman Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Bina Nusatara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies. © 2023 IEEE.

关键词： Adversarial Domain Adaptation Domain Shifting Problem MNIST Transfer Learning

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Perfomance Improvement of mpdCNN as Ensemble Learning Model on Low Resolution Image-Face Recognition System 8

Perfomance Improvement of mpdCNN as Ensemble Learning Model ...

引用

8th International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2025

作者： Wangean, Daniel Anando Suhartono, Derwin Bina Nusantara University Master of Computer Science Binus Graduate Program Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331520762

Low resolution image-face recognition system is one of the challenging aspects of face recognition models' development. From machine learning, deep learning, and into ensemble learning are implemented to develop face recognition models. Including mpdCNN that published by Mishra in a journal article. This mpdCNN already achieved 88.6% accuracy score on SCFace dataset while published. Based on literature review, ensemble learning model can be improved by modifying parallel layers. The purpose of this research is to improve mpdCNN's performance by implementing some modifications include adding parallel layers, alternating the fusion layers, expanding the layers, and adding residual connections. Some modifications were successfully improved mpdCNN's accuracy score. By adding parallel layers and residual connections into mpdCNN's architecture, the modified mpdCNN that proposed in this research achieved 92.33% accuracy score, measured by Rank-k evaluation metric. © 2025 IEEE.

关键词： Face recognition

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A systematic literature review of machine learning application in COVID-19 medical image classification 7

A systematic literature review of machine learning applicati...

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7th International Conference on computer Science and Computational Intelligence, CSCI 2022

作者： Daniel Cenggoro, Tjeng Wawan Pardamean, Bens Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how far research has progressed and what lessons can be learned for future research in this sector. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. According to the findings, chest X-ray were the most commonly used data to categorize COVID-19 and transfer learning techniques were the method used in this study. Researchers also concluded that lung segmentation and use of multimodal data could improve performance. © 2022 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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Efficient Hyperparameter Optimization Using Deep Q-Network and BRKGA 13

Efficient Hyperparameter Optimization Using Deep Q-Network a...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Kobayashi, Kosei Aritsugi, Masayoshi González, Pedro Henrique Mendonça, Israel Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9798350308365

Hyperparameter optimization (HPO) is paragon to maximize performance when designing machine learning models. Among different HPO methods, Genetic Algorithm (GA) based optimization is considered effective because it allows a wide and diverse range of solutions to be explored. However, GA's exploratory nature makes this type of algorithm to evaluate many solutions that do not improve the overall performance. This is specially costly when the objective function to be evaluated is time-consuming, like in the HPO field. In this paper, we propose an efficient hybrid algorithm that is able to reduce computational cost by combining deep reinforcement learning with the Biased Random Key Genetic Algorithm (BRKGA), a variant of genetic algorithms. Our reinforcement learning agent has a decision-making role during the population's fitness calculation, in which it filters out chromosomes that would not improve the overall fitness of the population. The agent uses small amounts of pre-trained data to identify trends in potentially good solutions, and carry out its decision process. We conduct experiments on eight different datasets to assess the effectiveness of the proposed method, and the results show that the proposed method can significantly reduce the computation time of hyperparameter search using BRKGA (up to 44% reduction in computational time) without compromising the quality of the solution (no statistically difference in results). © 2024 IEEE.

关键词： Genetic algorithms

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Blockchain-Based Chain of Custody for Digital Evidence with Hyperledger Fabric

Blockchain-Based Chain of Custody for Digital Evidence with ...

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International Conference on Information Networking

作者： Leandro Loffi Gerson Luiz Camillo Carla Merkle Westphall Carlos Becker Westphall Department of Post-Graduate Program in Computer Science Federal University of Santa Catarina (UFSC) Florianópolis SC Brazil

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

The increasing presence of digital evidence in legal, criminal, and civil cases requires adaptations to traditional chain-of-custody processes to ensure the integrity of this evidence. This work proposes the use of blockchain technology, specifically through Hyperledger Fabric, to strengthen the chain of custody for digital evidence. The developed solution employs smart contracts to immutably record each stage of evidence handling, from collection to transfer of custody, ensuring data authenticity, integrity, and traceability. Our implementation demonstrates that blockchain technology can significantly reduce the risk of evidence tampering, improve process efficiency, and facilitate audits, thereby contributing to greater reliability in the presentation of evidence during judicial proceedings.

关键词： Surveys Accuracy Distributed ledger Tracking Forensics Data security Smart contracts Fabrics Blockchains Reliability

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Exploring Transfer Learning Approach for Environmental Sound Classification: A Comparative Analysis

Exploring Transfer Learning Approach for Environmental Sound...

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Smart Computing, IoT and Machine Learning (SIML), International Conference on

作者： Theresia Herlina Rochadiani Yulyani Arifin Derwin Suhartono Widodo Budiharto Computer Science Department Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350364101

ISBN: (纸本)9798350364118

The expansion of deep learning techniques, as well as the availability of large audio/sound datasets, have fueled tremendous breakthroughs in audio/sound classification during the last several years. The transfer learning approach has emerged as one of the primary approaches for improving the accuracy and durability of classification systems. This study conducts a comprehensive comparative analysis to determine the effectiveness and performance of this method in environment sound classification. This current investigation focuses on environmental sound classification using VGGish and YAMNet pre-trained models with the ESC-50 and BDLib2 datasets. In the ESC-50 dataset, VGGish improves accuracy to 372.22%, while YAMNet improves accuracy to 383.33% when compared to baseline models. Similarly, in the BDLib2 dataset, accuracy increases significantly to 221.43% with VGGish and 246.43% with YAMNet. Transfer learning exhibits remarkable effectiveness in enhancing model performance, with significant accuracy boosts observed in both datasets. YAMNet, designed specifically for sound classification tasks, surpasses VGGish in improving environmental sound classification performance, potentially due to its architecture’s adaptability and diverse training on environmental sounds.

关键词： Training Deep learning Adaptation models Analytical models Accuracy Computational modeling Transfer learning

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Optimization of Node Reduction Using BRKGA for GNN-Based Traffic Speed Forecasting 13

Optimization of Node Reduction Using BRKGA for GNN-Based Tra...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Inokuchi, Yuto González, Pedro Henrique Mendonça, Israel Aritsugi, Masayoshi Graduate School of Science and Technology Kumamoto University Kumamoto Japan Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350308365

While Spatio-Temporal Graph Convolutional Networks (STGCNs) are an effective method for traffic speed fore-casting, their training and inference tend to be time-consuming. In this paper, we aim to refine these networks by strategically reducing their number of nodes, thereby boosting computational efficiency. The nodes in these graphs represent data observed for road segments, and by analyzing the interconnections and layout of the graph, we can identify nodes with minimal contribution to overall performance. Removing these nodes can potentially decrease computation time while maintaining the prediction accuracy. We employ the Biased Random-Key Genetic Algorithm (BRKGA) to identify a good set of nodes for removal, based on a predefined percentage reduction of the original graph size (e.g., retaining 95 % of the original graph). We evaluate different graph size configurations, ranging from 95 % to 70 % node retention, to determine the least impactful node set performance. Our experiments on three real-world datasets reveal that reducing nodes can decrease computation time by up to 29%, and as a byproduct of removing noise, even improve the prediction accuracy. © 2024 IEEE.

关键词： Genetic algorithms

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Confidentiality. Integrity, and Availability (CIA) Approach for Private Dataset Management System Design 9

Confidentiality. Integrity, and Availability (CIA) Approach ...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Ariansyah, Dedy Rahutomo, Reza Pardamean, Bens School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Dataset management systems are essential for assisting research and development (R&D) organizations in applying data governance protocols, especially in managing the utilization of datasets. In R&D, datasets are vital sources for producing analysis, machine learning prediction, and supporting decision-making. As such, it is important to deploy dataset management systems with considerations regarding the aspects of Confidentiality, Integrity, and Availability (CIA) which is a design principle that emphasized information protection and collaboration facilitation among dataset users. The study proposed an analysis and a design for a dataset management system that takes CIA aspects into account. In the future, the proposed design will be utilized as a benchmark for building a dataset management system. In this study, the Waterfall method was adopted. A use case diagram, an ERD (Entity Relationship Diagram), and an activity diagram were constructed in the design stage. In the project implementation stage, the Laravel framework including Laravel Jetstream was considered to be used. © 2024 IEEE.

关键词： Data integrity

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Application of 3D Printing and Design in Healthcare: A Focus on Undergraduate Research to Attract Students to Bioengineering and Related Disciplines 54

Application of 3D Printing and Design in Healthcare: A Focus...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Zelada-Quiroz, Cindy M. Reyes-Santiago, Brian L. Ortiz-Rivera, Eduardo I. University of Puerto Rico at Mayagüez Bio-Engineering Graduate Program Mayagüez Puerto Rico University of Puerto Rico at Mayagüez Department of Computer Science and Engineering Mayagüez Puerto Rico University of Puerto Rico at Mayagüez Department of Electrical and Computer Engineering Mayagüez Puerto Rico

ISBN: (纸本)9798350351507

This research-to-practice paper describes developing and analyzing state-of-the-art smart boots created by combining CAD technology and advanced 3D printing techniques to attract students in bio-engineering and related fields. The primary objective of this innovative immobilization boot is to expedite fracture recovery phases through an ergonomic design to ensure optimal patient comfort during its use. Technological solutions are crucial in aiding the rehabilitation process for fractures caused by falls, heavy lifting, or rotational trauma. However, cost and comfort-related issues persist, underscoring the need for alternative approaches. This research addresses these challenges and delves into the broader implications of fracture treatment, catalyzing future projects and investigations in bioengineering. Additionally, this study serves as an educational tool that sparks the interest of high school and engineering students, promoting multidisciplinary collaboration in innovation. By involving students in specialized courses covering 3D design, human bone anatomy, biology, and materials science, this initiative empowers them to deepen their knowledge and develop new technologies to address bone injury problems. Material analyses include evaluating the type of material depending on the fracture site, such as PLA for printing and cotton and silicone gel for the midsection between the splint and the body. This research aims to advance our understanding of the type of fracture, the methods associated with their treatment, and tissue repair processes during bone callus formation. To summarize, this multidisciplinary approach drives advancements in bio-engineering and related fields, aiming to enhance patient outcomes and inspire students to pursue further research in bio-engineering and related fields. As part of this endeavor, a list of university-level courses based on the experience of the University of Puerto Rico at Mayaguez (UPRM), such as biology, bio-materials, 3D

关键词： Students

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