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Improved Chest X-Ray Image Quality Using Median and Gaussian Filter Methods 4

Improved Chest X-Ray Image Quality Using Median and Gaussian...

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4th International Conference on Informatics, Multimedia, Cyber and information system, ICIMCIS 2022

作者： Kesuma, Lucky Indra Ermatita, Ermatita Erwin, Erwin Sari, Purwita Purabaya, Rudhy Ho Universitas Sriwijaya Doctoral Program in Engineering Faculty of Engineering Palembang Indonesia Universitas Sjakhyakirti India Universitas Sriwijaya Information System Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Departement of Computer System Palembang Indonesia Universitas Sriwijaya Informatic Management Faculty of Computer Science Palembang Indonesia Universitas Pembangunan Nasional Veteran Jakarta Information System Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665473279

The lungs are one of the organs of the body that are responsible for the human respiratory process and are very susceptible to dangerous diseases. For this reason, early detection and diagnosis of lung organs is needed, one of which is through examination of Chest X-Ray (CXR) images. Examination of the results of CXR images is still done manually by doctors and radiologists, this requires time and high accuracy. To facilitate the examination, it is necessary to image quality enhancement in order to get better image quality results so as to produce an accurate diagnosis. The initial stage in this research is to apply the contour improvement technique to the image using Morphology Opening, and followed by noise reduction using Median Filter and Gaussian filter. The results of the two methods of noise reduction are compared with the results of image quality in order to find out the best method that can be applied. The implementation of image quality enhancement results was measured quantitatively using Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Metrics (SSIM). In the Morphology Opening and Median Filter methods, the values obtained are 39.187, 22.252, and 0.952, respectively. Meanwhile, the Morphology Opening and Gaussian Filter methods obtained values of 38.717, 23.917 and 0.956. Based on these results, it can be concluded that both methods are able to improve image quality well. © 2022 IEEE.

关键词： Image quality

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Comparison Analysis of Intrusion Detection using Deep Learning in IoT Networks 11

Comparison Analysis of Intrusion Detection using Deep Learni...

引用

11th International Conference on information and Communication Technology, ICoICT 2023

作者： Ikhwan, Syariful Purwanto, Purwanto Rochim, Adian Fatchur Diponegoro University Doctoral Program of Information System School of Postgraduate Studies Semarang Indonesia Institut Teknologi Telkom Purwokerto D3 Telecommunication Engineering Banyumas Indonesia Diponegoro University Faculty of Engineering Department of Chemical Engineering Semarang Indonesia Diponegoro University Faculty of Engineering Department of Computer Engineering Semarang Indonesia

ISBN: (纸本)9798350321982

This study focuses on network security for Internet of Things (IoT) devices, which are particularly vulnerable to attack due to limited security measures. To protect IoT networks from attackers, the research implemented intrusion detection on the networks. The study compared several Deep Learning algorithms, including DNN, CNN, LSTM, and AE, to identify the most effective algorithm for solving network security problems using intrusion detection. The research used the UNSW-NB15 dataset for testing and employed binary classification for evaluation. The results showed that the DNN algorithm achieved an accuracy value of 99.76% and a loss value of 0.006%, outperforming the other algorithms. This study highlights the importance of implementing intrusion detection in protecting IoT devices and networks and demonstrates the efficacy of the DNN algorithm in detecting and preventing network security breaches. © 2023 IEEE.

关键词： Internet of things

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Power assist control for leg with HAL-3 based on virtual torque and impedance adjustment

Power assist control for leg with HAL-3 based on virtual tor...

引用

IEEE International Conference on systems, Man and Cybernetics

作者： S. Lee Y. Sankai Doctoral Program of System and Information Engineering University of Tsukuba Japan Institute of Systems and Engineering Mechanics University of Tsukuba Ibaraki Japan

This paper describes a method of power assist control for lower body based on neuromuscular signal, s-EMG (surface ElectroMyogram/Myoelectricity), and impedance adjustment around knee joint with the assist system, HAL (Hybrid Assistive Leg) -3 we have developed. Virtual Torque calculated by s-EMG enabled the HAL-3 to be operated as to intention of the experimental subject put on HAL, and assist the motion of lower body by predicting the moment around joints. Besides, the operator was able to swing the leg lighter by reducing the inertia and viscous friction around joint of the subject and HAL-3. In order to verify the proposed method, experiments for simple motion was performed with impedance values found by parameter identification with RLS (Recursive Least Square) method. The evaluation of assisted motion was done by Assist Efficiency (AE) calculated from s-EMG in nearly proportion to the operator's muscle force. The results showed the response of operational signal into actuator with impedance adjustment was improved dramatically, and the amplitudes of s-EMG were reduced significantly, then we could confirm the availability of impedance adjustment.

关键词： Torque control Leg Surface impedance Control systems Neuromuscular Knee Friction Parameter estimation Resonance light scattering Least squares methods

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A caching scheme for dynamic location management in PCS networks

A caching scheme for dynamic location management in PCS netw...

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IEEE Conference on Vehicular Technology (VTC)

作者： Chang Woo Pyo Jie Li Hisao Kameda Doctoral Program System & Information Engineering University of Tsukuba Ibaraki Japan Institute of Information Science & Elelctronics University of Tsukuba Ibaraki Japan

ISBN: (纸本)0780379543

This paper addresses caching for location management in personal communication service (PCS) networks. Caching is useful to reduce the delay of locating frequently retrial calls to a mobile user. However, the cached information may be obsolete due to the mobility of mobile users, which is the reason for the degradation of system performance. We introduce an efficient caching scheme for dynamic location management to improve the location management performance. Moreover, we establish an analytical model and perform a performance evaluation to study the performance improvement of the caching scheme over the non-caching scheme. We provide a simple and direct way of caching based on dynamic location management.

关键词： Intelligent networks Personal communication networks Databases engineering management Cities and towns Degradation system performance systems engineering and theory information science Delay

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A dynamic and distributed domain-based mobility management method for mobile IPv6

A dynamic and distributed domain-based mobility management m...

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IEEE Conference on Vehicular Technology (VTC)

ISBN: (纸本)0780379543

We address mobility management for the internet protocol version 6 in mobile networks, namely mobile IPv6 (MIPv6). To correctly transfer IPv6 packets to a mobile node (MN), the MN has to update its binding to the home network while roaming away from its home network. Mobile IP networks suffer high binding update traffic from high rates of handoffs and long packet delivery delay from the large distance to the home network. In this paper, we introduce a dynamic and distributed domain-based mobility management protocol for MIPv6 to reduce the binding update and packet delivery delay and traffic. We describe the basic protocol operations and examine the performance improvement of our mobility management over the previous mobile IP mobility management method by using simulations. We show that the dynamic approach provides the optimal mobility solution for MIPv6 and the distributed approach can decentralize the network load.

关键词： Mobile radio mobility management Home automation Protocols IP networks Telecommunication traffic Internet Cities and towns Delay engineering management systems engineering and theory

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A model of implicit association learning based on plasticity in the perirhinal cortex

A model of implicit association learning based on plasticity...

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International Conference on Neural information Processing

作者： S. Morokami A. Suemitsu M. Morita Doctoral Program in System Information Engineering University of Tsukuba Tsukuba Ibaraki Japan Doctoral Program in Engineering University of Tsukuba Tsukuba Ibaraki Japan Institute of Engineering Mechanics and Systems University of Tsukuba Tsukuba Ibaraki Japan

In the delayed match-to-sample task, responses of inferior temporal neurons to adjacent stimuli in the sequence are correlated to each other when the monkey was trained repeatedly with the sequence of visual stimuli, although the monkey was not required to associate the stimuli with each other. This correlation, however, is not observed for a monkey with lesions of the rhinal cortex, which is not consistently explained by existing models of such correlated responses. In the present study, we construct a model consisting of two networks corresponding to area TE and the perirhinal cortex, and show that perirhinal plasticity may underlie the mechanism of implicit association learning.

关键词： Brain modeling Neurons systems engineering and theory Lesions Tellurium Fractals Testing Delay effects

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A Risk-Benefit Analysis of Open Government Data Portals for Business

A Risk-Benefit Analysis of Open Government Data Portals for ...

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Computer system, information Technology, and Electrical engineering (COSITE), International Conference on

作者： Emigawaty Kusworo Adi Adian Fatchur Rochim Doctoral Program of Information System Universitas Diponegoro Yogyakarta Indonesia Department of Computer Engineering Universitas Diponegoro Yogyakarta Indonesia

Open Government Data (OGD) initiatives have the potential to provide various benefits to the business sectors, such as market insights analysis, new product innovation, cost savings, improved decision-making, enriched collaboration, and increased transparency. However, OGD also carries potential negative consequences, including data inaccuracy, low data quality, privacy and security, data bias and discrimination, and legal compliance. Thus, this paper aims to comprehensively understand the potential risks and benefits associated with OGD portals and their causality levels. To investigate the causal relationships involved, a Bayesian Belief Networks (BBNs) model was employed, which is constructed using an explanatory strategy based on six main phases. In this research, interview sessions were conducted with five business practitioners who have experience in accessing and using datasets on the OGD portals. The study’s results indicate that the use of datasets on the OGD portal has a moderate level of potential risk, especially in terms of low data quality. Additionally, the potential benefits of using the OGD portal are also significant, particularly in helping businesses make informed decisions to improve their product innovation and services. This paper’s contribution lies in comprehensively understanding the potential risks and benefits of Open Government Data (OGD) portals for business sectors.

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Multi Task Classification Using Deep Learning Approaches for Big Five Personality Traits Prediction: A Review 11

Multi Task Classification Using Deep Learning Approaches for...

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11th International Conference on information Technology, Computer and Electrical engineering, ICITACEE 2024

作者： Nudin, Salamun Rohman Gernowo, Rahmat Somantri, Maman Wibowo, Adi Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Diponegoro University Faculty of Engineering Department of Electrical Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350389289

This paper presents a comprehensive review of deep learning methods for the Big Five personality traits prediction using multi-task classification. The purpose of the review is to determine the performance of models developed in previous research to describe research opportunities and challenges in the future. The Big Five model is a framework in psychology that categorizes human personality into five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The review begins by analyzing 22 articles from the Science Direct, IEEE Xplore, and Scopus databases by applying screening using the Big Five Personality keywords. Then study the concepts of deep learning for model development such as: input, architecture, method, dataset, and performance to identify the strengths and limitations of each approach. The review results of the highest average accuracy by modality such as unimodal text with essay dataset (80.98%), unimodal image with ImageNet dataset (95.36%), unimodal audio with SSPNet dataset (79.48%), bimodal image and audio with Chalearn Video First Impression dataset (91.67%), multimodal text, image, and audio with Chalearn Video First Impression dataset (91.88%). The average accuracy of the Big Five Personality traits was highest (95.36%) and lowest (59%). This highlights the need for standard benchmarks, interpretable models and interdisciplinary collaboration to improve the accuracy, reliability and ethical implications of personality prediction. Future research is expected to pave the way for advancements in understanding human personality through deep learning approaches using other modalities such as EEG, GSR, and PPG. © 2024 IEEE.

关键词： Contrastive Learning

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Sub-Exact Sequence on Hilbert Space 3

Sub-Exact Sequence on Hilbert Space

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3rd International Conference on Applied Sciences Mathematics and Informatics, ICASMI 2020

作者： Utami, B.H.S. Fitriani Usman, M. Warsono Daoud, J.I. Doctoral Program of Mathematics and Natural Science Faculty of Mathematics and Natural Science University of Lampung Bandar Lampung Indonesia Department of Information System Stmik Pringsewu Pringsewu Indonesia Department of Science and Engineering Faculty of Engineering International Islamic University Malaysia P.O. Box 10 Kuala Lumpur50728 Malaysia

The notion of the sub-exact sequence is the generalization of exact sequence in algebra especially on a module. A module over a ring R is a generalization of the notion of vector space over a field F. Refers to a special vector space over field F when we have a complete inner product space, it is called a Hilbert space. A space is complete if every Cauchy sequence converges. Now, we introduce the sub-exact sequence on Hilbert space which can later be useful in statistics. This paper aims to investigate the properties of the sub-exact sequence and their relation to direct summand on Hilbert space. As the result, we get two properties of isometric isomorphism sub-exact sequence on Hilbert space. © Published under licence by IOP Publishing Ltd.

关键词： Hilbert spaces

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Comparison of Feature Selection Methods on Medical Record Data

Comparison of Feature Selection Methods on Medical Record Da...

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Modeling & E-information Research, Artificial Learning and Digital Applications (ICMERALDA), International Conference on

作者： Lastri Widya Astuti Ermatita Dian Palupi Rini Student of Informatic Engineering Doctoral Program Sriwijaya University Palembang Indonesia Information System Sriwijaya University Palembang Indonesia Informatic Engineering Sriwijaya University Palembang Indonesia

Medical record data are an essential part of medicine, but the effective use of data has become difficult due to the rapid increase in data. Determining the selected features allows researchers to extract the information from medical data. Feature selection in classification provides the advantage of reduced time and storage. In addition, feature selection methods can improve performance by removing redundant or irrelevant data based on their weights. The benefits of feature selection are that it speeds up the training time, simplifies the model, makes interpretation easier, and improves generalization by reducing overfitting. Feature selection in this research uses two methods: Binary Whale Optimization (BWOA), Particle Swarm Optimization, and Hybrid with Gray Wolf Optimization (PSOGWO). Two feature selection methods were compared to determine the most optimal method for the classification process. A comparison of the two feature selection methods shows that the classification process with many attributes produces an optimal classification process, where the number of representative variables in the breast cancer dataset in the BWOA method has five features, and the PSOGWO method has 11 attributes. Meanwhile, the highest accuracy results in breast cancer classification using the BWOA average of 95.90% and PSOGWO Method average of 97.64%

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