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2024 International Conference on Information technology Research and Innovation, ICITRI 2024

作者： Mariyanto Pardede, Hilman Ferdinandus Nusa Mandiri University Nusa Mandiri University Graduate School in Computer Science Faculty of Information Technology Jakarta Indonesia National Research and Innovation Agency Indonesia Nusa Mandiri University Computer Science Faculty of Information Technology Jakarta Indonesia

ISBN: (纸本)9798350376210

Studies show that mosquitos could be identified using their recording during their flights with machine learning. However, differences in environmental conditions and the use of various recording devices can cause significant variations in audio channels making their performances drop. Models created with Machine Learning are usually less robust to audio channel variations. This research aims to increase the robustness of the model in identifying Aedes Aegypti and Aedes Albopictus through audio spectral analysis. The approach used involves audio manipulation techniques such as normalization, channel equalization, and histogram equalization. Modeling is performed using various machine learning architectures, including Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Gated CNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Feature extraction involves ShortTime Fourier Transform (STFT), Filter Bank (FBank), MelFrequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), and Power Normalized Cepstral Coefficients (PNCC). Test results show that the combination of histogram equalization, MLP, and GFCC produces optimal performance. On the Wingbeats dataset, the highest accuracy rate reached 93.82%. Apart from that, this model also shows good performance on the Humbugdb dataset with an accuracy of 83.08% and on the Dryad dataset with an accuracy of 75.86%. These findings provide a valuable contribution in developing a robust model and can be a solid basis for supporting efforts to monitor and control diseases caused by Aedes Aegypti and Aedes Albopictus mosquitoes. © 2024 IEEE.

关键词： Long short-term memory

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A New Model to Evaluate Signature and Anomaly Based Intrusion Detection in Medical IoT System Using Ensemble Approach

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SN computer science 2025年第4期6卷 1-20页

作者： Abdullah, A. Sheik Sunil, Hridhik John Nazmudeen, Mohamed Saleem Haja School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Dean of Graduate Studies and Research Office Universiti Teknologi Bandar Seri Begawan Brunei Darussalam

The Internet of Medical Things (IoMT) domain, also called clinical IoT systems, is quite helpful for the simulation of devices and applications concerned with clinical informatics. The execution of this platform needs a simulating model to be efficiently mapped towards the working functionality for efficient delivery of quality healthcare at any cost. This research presents a new model for web intrusion detection in IoMT systems by developing machine learning algorithms based on ensemble learning. Our approach collects, analyzes, and organizes relevant and suspect data traffic during training in machine learning algorithms. The experimentation was carried out using different cohort methods, and I learned how to better identify and deliver attacks more effectively by tabulating the findings. Our approach, which focuses on the medical system incorporating Internet Sensors, incorporates continuous traffic monitoring in and out of the system. Using properties such as source and destination IP addresses, packet sizes, protocols, etc., we have classified the traffic accordingly with signatures and anomalies. The ensemble modelling of our proposed approach using XG boost provides an improved accuracy of about 99.67% with a P-value < 0.001, respectively. The visual data exploration has been made using ROC graph analysis for all models, with a roc-aug score of 0.9963 for KNN, 0.9965 for the Decision tree, 0.9966 for the random forest,0.9965 for XG-boost and 0.9946 for the Ada- Boost model. This analysis provides an intelligent attack, Practical insights, and a strategy that can be used to improve the cybersecurity of healthcare systems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data security Ensemble learning Healthcare modeling Intrusion detection system IoMT Machine learning

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Long Short-Term Memory-based Models for Sleep Quality Prediction from Wearable Device Time Series Data 8

Long Short-Term Memory-based Models for Sleep Quality Predic...

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

作者： Hidayat, Alam Ahmad Budiarto, Arif Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Several studies suggest that sleep quality is associated with physical activities. Moreover, deep sleep time can be used to determine the sleep quality of an individual. In this work, we aim to find the association between physical activities and deep sleep time by modeling the time series data such as heart rate and a number of steps captured from a commercial wearable device. Our previous study demonstrates that deep learning-based time series modeling is well suited for our problem since the temporal patterns in the two physical parameters need to be captured to obtain more accurate results. We first preprocess our series data to have a time-step size of 10 minutes. To improve our previous effort in this modeling, we compare four different variants of Long Short-Term Memory (LSTM)-based models, ranging from single input to dual input models. Our result shows that the simple stacked LSTM model performs better for our data because the remaining models suffer from overfitting due to a larger number of the trained parameters. © 2023 The Authors. Published by Elsevier B.V.

关键词： deep learning forecasting sleep quality time series wearable devices

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A HIGH-PRECISION 3D LASER MEASUREMENT METHOD BASED ON ESTIMATION OF TRUE VALUES BY HIGH-DENSITY MEASUREMENTS AND CALCULATION OF DIFFERENCES BETWEEN MEASUREMENT DATA OF TWO TIMES

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AIJ Journal of technology and Design 2022年第68期28卷 515-520页

作者： Kakizaki, Ken'ichi Mukai, Tomohisa Araki, Shunsuke Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Dept. of Structural Engineering Building Research Institute

3D laser measurement has a problem that the measurement precision is not sufficient for damage analysis of buildings. In addition, when there is an unevenness in the measurement target, it is difficult to distinguish whether it is an initial shape or damage. In this paper, we propose a method to reduce the measurement error by processing the high-density laser measurement data with statistical methods. In addition, we propose a method to detect the damaged area by comparing the initial shape data of the measured object with the newly measured data. © 2022 Architectural Institute of Japan. All rights reserved.

关键词： Measurement errors

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Integrating Spatial Prior Adapter for Enhancing SAM Performance in Medical Image Segmentation 12

Integrating Spatial Prior Adapter for Enhancing SAM Performa...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Hu, Jihong Li, Yinhao Lin, Lanfen Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Kusatsu Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350340181

The Segment Anything Model (SAM) has emerged as a focal point of interest within the domain of image segmentation. Capitalizing on extensive pretraining with vast datasets and an interactive prompting mechanism, SAM stands out as a formidable base model for natural image segmentation tasks. However, a number of empirical studies indicate that SAM's performance diminishes substantially when applied to standard medical segmentation tasks. This is largely attributed to the modal and feature disparities between natural and medical images. In response to this challenge, this study introduces a novel, parameter-efficient fine-tuning approach termed the Spatial Prior Adapter (SPA). SPA leverages convolutional neural networks (CNNs) to capture multi-scale features, which are seamlessly amalgamated with the features harnessed from the SAM image encoder. Notably, during the model's fine-tuning phase, the SAM image encoder is frozen, enabling exclusive refinement of the SPA for optimal parameter calibration. Experimental evaluations on two medical segmentation datasets validate the effectiveness of our proposed method. © 2023 IEEE.

关键词： Image segmentation

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"Nyctophy": Assessing the effectiveness of serious game for nyctophobia based on virtual reality by usability and heart rate monitoring

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Procedia computer science 2024年 245卷 185-192页

作者： Elshad Ryan Ardiyanto Patrick Alexander Edyth Novian Putra Dimas Ramdhan Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta

Nyctophobia is a phobia of the dark and is common among children but also found in adults. While the phobia itself is commonly known, the diversity of its treatment is still minimal. As technology has reached its highest point, numerous inventions and innovations have emerged in the medical sector. This study aimed to examine the usability of Virtual Reality and Keyboard and Mouse as an interface using the Extended Reality Game Usability Questionnaire (XR-GUQ) in Nyctophobia therapy and to analyze the heart rate response, impact, and implications using t-test Paired/Wilcoxon Signed Rank. Thirty-four Bina Nusantara University students from various backgrounds completed the test using interfaces and a smartwatch. They were divided into two groups, high- and low fear, using the modified Acrophobia Questionnaire (AQ), which resulted in a significant difference. The XR-GUQ result was insignificant in all variables, which indicates similar usability scores on both interfaces. The t-test Paired/Wilcoxon Signed Rank test revealed a significant difference between the high- and low-fear groups when using the Virtual Reality interface compared to the Keyboard and Mouse interface. This significant difference was found within each group regardless of the interface used. This research shows similar scores in terms of usability but a significant difference in heart rate response with the help of a serious game to give a new sensation of phobia therapy.

关键词： Nyctophobia Virtual Reality Keyboard and Mouse Usability Heart Rate Serious Game Effectiveness

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MARS: An Adaptive Multi-Agent DRL-based Scheduler for Multipath QUIC in Dynamic Networks 31

MARS: An Adaptive Multi-Agent DRL-based Scheduler for Multip...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Han, Xueqiang Han, Biao Li, Ruidong Ji, Xiaolan College of Computer National University of Defense Technology Changsha China Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan

ISBN: (纸本)9798350399738

The multipath extension of the Quick UDP Internet Connection (QUIC) protocol, also called MPQUIC, is currently attracting increasing attention from both industry and academia. The multipath scheduler of MPQUIC determines how to distribute the packets onto different paths. However, our experimental results show that they fail to adapt to various receive buffer sizes and Quality of Service (QoS) requirements while applying current multipath schedulers into MPQUIC due to the diversity of devices and applications. These problems are especially severe under heterogeneous and dynamic network environments. To tackle these problems, we propose MARS, a Multi-Agent deep Reinforcement learning (MADRL) based Multipath QUIC Scheduler, which is able to promptly adapt to dynamic network environments. It exploits the MADRL method to learn a neural network for each path and generate scheduling policy. Besides, it introduces a novel multi-objective reward function that takes out-of-order (OFO) queue size and different QoS metrics into consideration to realize adaptive scheduling optimization. We implement MARS in an MPQUIC prototype and compare it with the state-of-the-art multipath schedulers in both emulated and real-world networks. Experimental results show that MARS outperforms the other schedulers with better adaptive capability regarding the receive buffer sizes and QoS. © 2023 IEEE.

关键词： Reinforcement learning

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Algorithms of Sampling-Frequency-Independent Layers for Non-integer Strides 31

Algorithms of Sampling-Frequency-Independent Layers for Non-...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Imamura, Kanami Nakamura, Tomohiko Takamune, Norihiro Yatabe, Kohei Saruwatari, Hiroshi Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan Department of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology Tokyo Japan

ISBN: (纸本)9789464593600

In this paper, we propose algorithms for handling non-integer strides in sampling-frequency-independent (SFI) convolutional and transposed convolutional layers. The SFI layers have been developed for handling various sampling frequencies (SFs) by a single neural network. They are replaceable with their non-SFI counterparts and can be introduced into various network architectures. However, they could not handle some specific configurations when combined with non-SFI layers. For example, an SFI extension of Conv-TasNet, a standard audio source separation model, cannot handle some pairs of trained and target SFs because the strides of the SFI layers become non-integers. This problem cannot be solved by simple rounding or signal resampling, resulting in the significant performance degradation. To overcome this problem, we propose algorithms for handling non-integer strides by using windowed sinc interpolation. The proposed algorithms realize the continuous-time representations of features using the interpolation and enable us to sample instants with the desired stride. Experimental results on music source separation showed that the proposed algorithms outperformed the rounding- and signal-resampling-based methods at SFs lower than the trained SF. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Network architecture

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Enhanced Attitude Stabilization of Quadrotors Using a Single-Axis Constraint Moment Disturbance Obserner

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IEEJ Transactions on Industry Applications 2024年第4期144卷 257-264页

作者： Ichiryu, Atsuhiro Shimada, Akira Electrical Engineering and Computer Science Graduate School of Engineering and Science Shibaura Institute of Technology 3-7-5 Toyosu Koto-ku Tokyo135-8548 Japan Dept. of Engineering and Design Shibaura Institute of Technology 3-7-5 Toyosu Koto-ku Tokyo135-8548 Japan

This study proposes a maintaining a quadrotor’s attitude system using a ‘moment disturbance observer’ aimed at enhancing stability. In recent years, quadrotors have become increasingly prevalent in sectors such as agriculture, surveying, logistics, aerial photography, infrastructure inspection, and security. However, with their growing, there have been concerns about potential physical and human harm stemming from accidents. The Ministry of Land, Infrastructure, Transport, and Tourism has reported accidents attributed to challenging maneuvering conditions that have resulted in crashes. This study aims to develop a quadrotor that can maintain a stable attitude during sudden and significant alterations in its orientation. We suggest employing a moment disturbance observer to estimate the necessary moments for stable attitude maintenance. Numerical simulation results show that the proposed method surpasses traditional PID control in terms of attitude stabilization. Furthermore, this method resulted in superior attitude stability in single-axis experiments with a mechanically constrained quadrotor compared to conventional PID control methods. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Aerial photography

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Investigation of Effective Electrode Structure for Metal Removal from Composite Materials Using Pulsed Discharge 10

Investigation of Effective Electrode Structure for Metal Rem...

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10th Euro-Asian Pulsed Power Conference, 25th International Conference on High-Power Particle Beams and 20th International Symposium on Electromagnetic Launch technology, EAPPC-BEAMS-EML 2024

作者： Doi, Takuto Oishi, Shota Sakugawa, Takashi Nishimura, Kaisei Shobako, Shinichiro Shimasaki, Shinichi Yamashita, Tomohiko Kumamoto University Graduate School of Science and Technology Department of Computer Science and Electrical Engineering Kumamoto Japan Kagawa College National Institute of Technology Department of Electro-Mechanical Systems Engineering Takamatsu Japan

ISBN: (纸本)9789038661353

The authors investigate the potential of pulsed power technology in recycling of E-waste. Applying the pulsed discharge can separate composite materials into plastic and metal. In this study, pulsed discharge was applied to indium tin oxide (ITO) coated plastic films. We used two electrode types: rod-To-rod electrode and a pair of flat plate electrodes to investigate the effect of electrode structure on the metal removal area in detail. A series of single pulse discharges were applied at various electrode distances. As a result, it was revealed that metal can be removed over a wide range by the pulsed discharge using a pair of flat plate electrodes. When the gap between the electrodes was 30 mm, the removal area by the flat plate electrodes was approximately 3.4 times that by the rod electrodes. Analysis of the current density also revealed that the metal removal area was greatly affected by the current density. © 2024 Eindhoven University of technology.

关键词： Tin oxides

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