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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Arif Ridho Lubis Santi Prayudani Julham Okvi Nugroho Yuyun Yusnida Lase Muharman Lubis Department of Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia SMA Muhammadiyah 2 Medan Indonesia School of Industrial Engineering Telkom University Bandung Indonesia

Customer churn is a situation that receives extensive analysis using a variety of techniques from data mining or machine learning. Data mining techniques may be used to anticipate customer churn. A data mining algorithm was used in this study to forecast client turnover. Logistic Regression and gradient boosting models were employed as one of the data mining techniques for implementing customer churn forecasts. The gradient boosting model could be said to perform better in predicting customer churn when compared to the logistic regression model, which produced accuracy training 87% and testing 88%. The results showed that the gradient boosting model was able to carry out the training and testing process with a training accuracy of 93% and a testing accuracy of 91%.

关键词： Training Seminars Machine learning algorithms Predictive models Boosting Prediction algorithms Data mining

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Generalizable deep clustering based on Bi-LSTM with applications to sepsis and acute kidney disease populations

Generalizable deep clustering based on Bi-LSTM with applicat...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Tan, Yongsen Huang, Jiahui Zhuang, Jinhu Liu, Yong Huang, Haofan Yu, Xiaxia Wang, Fusheng Shenzhen University School of Biomedical Engineering Health Science Center Shenzhen China Shenzhen Hospital Southern Medical University Department of Intensive Care Unit Shenzhen China Stony Brook University Department of Biomedical Informatics Department of Computer Science Stony BrookNY United States

ISBN: (纸本)9781665468190

Despite the abundance of subphenotype clustering studies on sepsis and acute kidney injury (AKI), few models consider the real-time information of clinical features. The lack of supervision may lead to patient subgroups being derived as clusters without the stratification of patients based on the outcome of interests. The sensitivity of the dimension in clustering methods is generally ignored, so clusters lack robustness. In this study, we propose an ensembled outcome-driven bidirectional long short-term memory autoencoder (BiLSTM-AE) architecture with high robustness and transferability to identify subphenotypes. BiLSTM-AE learns the advanced representation of the time-series clinical features by co-training the encoder and a weak predictor to achieve the risk-stratified clustering of patients. Clusters of a variety of dimensions are ensembled to combine global and local information. Four different datasets from three public datasets, MIMIC-III-AKI, MIMIC-IV-sepsis, eICU-AKI, and eICU-sepsis, were used to assess the method's effectiveness in clustering and prediction. Compared to baseline approaches including latent class analysis (LCA), subgroups generated by BiLSTMAE exhibited the highest mortality risk ratios between subgroups: the mortality for subphenotypes 1, 2, and 3 of BiLSTM and LCA was 6.91%, 17.53%, and 75.56% vs. 13.2%, 14.4%, and 19.7% for MIMIC-III-AKI. The prediction metric area under the receiver operating characteristic curve was 0.86 for MIMIC-IIIAKI, 0.91 for eICU-AKI, 0. SS for MIMIC-IV-sepsis, and 0. S9 for eICU-sepsis. Additionally, clinical evaluation of BiLSTM-AE generated subgroups revealed more meaningful distributions of member characteristics across subgroups. Thus, the method is an effective means to consider the real-time information of clinical features. © 2022 IEEE.

关键词： Long short-term memory

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Development of Visual Dashboard for River Monitoring System 8

Development of Visual Dashboard for River Monitoring System

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8th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2022

作者： Ahmad, Yasser Asrul Gunawan, Teddy Surya Munir, Muhd Hisyamuddin Mohd Rasidi Al Yusoff, Nelidya Md. Kartiwi, Mira Mansor, Hasmah International Islamic University Malaysia Electrical and Computer Engineering Dept Kuala Lumpur53100 Malaysia Razak Faculty of Tech. and Informatics Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia Information Systems Department International Islamic University Malaysia Kuala Lumpur53100 Malaysia

ISBN: (数字)9781665488006

ISBN: (纸本)9781665488006

Monitoring a tributary's water depth and velocity can provide a wealth of information for ecological systems. Typically, tributaries are located deep within the jungle, and manual measurement is the norm. The research aims to develop a real-time remote monitoring system for measuring water depth and velocity. The system uses ultrasonic and water flow sensors to measure velocity and river depth. The data will be transmitted to the back-end software, including Arduino IDE and Node-RED, for data collection. The data is then stored in the cloud database InfluxDB for future use. The data can be set to be automatically deleted every 1 hour, 1 day, 1 month, or at other time intervals. The Grafana platform can produce an interactive Graphical User Interface (GUI), and the administrator can modify the visual dashboard using either their program code or existing features. It can then be made available to the general public as an observer for the online visual dashboard via the Uniform Resource Locator (URL). © 2022 IEEE.

关键词： Remote control

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Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution

arXiv

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arXiv 2022年

作者： Zhang, Jiahui Zhan, Fangneng Yu, Yingchen Wu, Rongliang Zhang, Xiaoqin Lu, Shijian The School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Max Planck Institute for Informatics Germany The Wenzhou University China

Recently, single image super-resolution (SR) under large scaling factors has witnessed impressive progress by introducing pre-trained generative adversarial networks (GANs) as priors. However, most GAN-Priors based SR methods are constrained by an attribute disentanglement problem in inverted latent codes which directly leads to mismatches of visual attributes in the generator layers and further degraded reconstruction. In addition, stochastic noises fed to the generator are employed for unconditional detail generation, which tends to produce unfaithful details that compromise the fidelity of the generated SR image. We design LAREN, a LAtent multiRelation rEasoNing technique that achieves superb large-factor SR through graph-based multi-relation reasoning in latent space. LAREN consists of two innovative designs. The first is graph-based disentanglement that constructs a superior disentangled latent space via hierarchical multi-relation reasoning. The second is graph-based code generation that produces image-specific codes progressively via recursive relation reasoning which enables prior GANs to generate desirable image details. Extensive experiments show that LAREN achieves superior large-factor image SR and outperforms the state-of-the-art consistently across multiple benchmarks. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Triple-band Modified Printed Inverted-F Antenna Design for WI-FI-7 Applications

Triple-band Modified Printed Inverted-F Antenna Design for W...

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European Conference on Antennas and Propagation, EuCAP

作者： Lazaros A. Iliadis Vasileios P. Rekkas Achilles D. Boursianis Panagiotis Sarigiannidis George K. Karagiannidis Christos Christodoulou Sotirios K. Goudos ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Informatics and Telecommunications Engineering University of Western Macedonia Kozani Greece Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Engineering and Computing The University of New Mexico USA

WI-FI-6/6E is now commercialized and the WI-FI community is currently developing the IEEE 802.11be standard, namely WI-FI-7, which will offer enhanced throughput and higher data rate than its predecessors. In this article, a compact triple-band printed inverted-F (IF) antenna operating at 2.4 GHz, 5 GHz, and 6 GHz frequency bands is designed for WI-FI-7 applications. We design a novel antenna structure that is well-suited for triple-band operation. The core idea is to use a stripline as a feeder that also couples two modified IF designs. A nature-inspired optimization method, namely the artificial hummingbird algorithm (AHA), is used to achieve an optimal design solution for the triple-band IF antenna. Computed results demonstrate that the proposed antenna achieves satisfactory results regarding the reflection coefficient and the realized gain in all the frequency bands of interest.

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Alternating optimization for multimodal collaborating odometry estimation in CAVs

Alternating optimization for multimodal collaborating odomet...

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Mediterranean Conference on Control and Automation (MED)

作者： Nikos Piperigkos Aris S. Lalos Kostas Berberidis Computer Engineering and Informatics Dept University of Patras Greece Industrial Systems Institute Athena Research Center Patras Science Park Greece

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Cooperative, Connected and Automated Mobility will enable the close coordination of actions between vehicles, road users and traffic infrastructures, resulting in profound socioeconomic impacts. In this context, location and yaw angle of vehicles is considered vital for safe, secured and efficient driving. Motivated by this fact, we formulated a multimodal sensor fusion problem which provides more accurate localization and yaw information than the original sources. Simultaneously estimating location and yaw parameters of vehicles can be treated as the task of cooperative odometry or awareness. To do so, V2V communication as well as multimodal self and inter-vehicular measurements from various sensors are considered for the problem formulation. The solution strategy is based on the maximum likelihood criterion as well as a novel alternating gradient descent approach. To simulate realistic traffic conditions, CARLA autonomous driving simulator has been used. The detailed evaluation study has shown that each vehicle, relying only on its neighborhood, is able to accurately re-estimate both its own and neighboring states (comprised of locations and yaws), effectively realising the vision of 360 ◦ awareness.

关键词： Location awareness Maximum likelihood estimation Visualization Laser radar Multimodal sensors Roads Vehicular ad hoc networks

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Attention-Based Multiscale Localization for Bone Age Assessment

Attention-Based Multiscale Localization for Bone Age Assessm...

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IEEE International Symposium on Biomedical Imaging

作者： Kyunghee Jung Toan Duc Nguyen Duc-Tai Le Junghyun Bum Chang-Hwan Son Hyunseung Choo Department of Superintelligence Engineering Sungkyunkwan University Korea College of Computing and Informatics Sungkyunkwan University Korea Sungkyun AI Research Institute Sungkyunkwan University Korea Department of Software Science & Engineering Kunsan National University Korea Department of Electrical and Computer Engineering Sungkyunkwan University Korea

Bone Age Assessment (BAA) is crucial for the biological maturity evaluation of children. Developing automated techniques of BAA has gained a lot of attention from both academia and medicine. This paper presents a novel deep-learning-based BAA method including refining and multi-scale processing of hand X-ray images. The refining step removes unnecessary background and noises in the images, resulting in a high-quality dataset. Such image refinement is beneficial for Region of Interest (ROI) localization step with self-attention mechanism. The localization model is trained with multiscale hand X-ray images separately to obtain multiple ROIs for each image. Eventually, the multiscale ROIs are used as complementary features of an image in training a regression model for BAA. We evaluate the performance of the proposed method using 2017 RSNA pediatric bone age challenge dataset. The results show mean absolute error of 3.52, which is 24.9% lower than state-of-the-art results.

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Cybersecurity of Sensors on Smart Vehicles: Review of Threats and Solutions

Cybersecurity of Sensors on Smart Vehicles: Review of Threat...

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International Conference of computer and informatics engineering (IC2IE)

作者： Prasetyo Adi Wibowo Putro Fetty Amelia Jan Pidanic Heru Suhartanto Imam Arif Rahardjo Ejo Imandeka Faculty of Computer Science Universitas Indonesia Depok Indonesia Cryptographic Hardware Engineering Politeknik Siber dan Sandi Negara Bogor Indonesia Faculty of Electrical Engineering and Informatics University of Pardubice Pardubice Czech Republic Department of Electrical Engineering Universitas Negeri Jakarta Jakarta Indonesia

The use of sensors in smart vehicles brings benefits and vulnerabilities. Different kinds of sensors in smart vehicles are vulnerable to cyber-attack. Until now, the investigation of challenges and solutions for in-vehicle cybersecurity hasn't discussed various sensor objects and their correlation. In this study, we studied the cyber security problems of sensors in smart vehicles and how to overcome them. The research was designed as Systematic Literature Review (SLR) using the Kitchenham methodology with modification in the filtering phase using the artificial intelligence application, Elicit, to identify the problems, conclusions, and methodology description. Seventeen publications from 2016 until 2023 were gained from five databases. As a result, we find that the most discussed object related to cybersecurity sensors on smart vehicles are Electronic Control Units. Spoofing and jamming is still the most addressed threat, and machine learning is the most utilized solution to be implemented in detection systems. Advanced detection systems are incorporating updated attack models. We also suggest using updated attack models and machine learning algorithms to ensure the safety and security of smart vehicle technology. All identified sensor technology correlated using mind maps under the Intelligent Transport System theory.

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Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms

arXiv

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arXiv 2023年

作者： Saifullah, Shoffan Drezewski, Rafal Yudhana, Anton Pranolo, Andri Kaswijanti, Wilis Suryotomo, Andiko Putro Putra, Seno Aji Khaliduzzaman, Alin Prabuwono, Anton Satria Japkowicz, Nathalie Institute of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Center of Knowledge Engineering and Data Science Department of Electrical Engineering and Informatics Universitas Negeri Malang Malang65145 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia College of Computer and Information Hohai University Nanjing211100 China Department of Informatics Faculty of Industrial Technology Universitas Ahmad Dahlan Yogyakarta55166 Indonesia Faculty of Agricultural Engineering and Technology Sylhet Agricultural University Sylhet3100 Bangladesh Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science American University WashingtonDC20016 United States

This study explored the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection for precise poultry hatchery practices. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset (200 single egg images) using augmented images (rotation, flip, scale, translation, and reflection). The training results demonstrated that all models achieved high accuracy, indicating their ability to learn and classify chicken eggs’ fertility. However, variations in accuracy and performance were observed when these models were evaluated on the testing datasets. The InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. It demonstrated excellent performance in all parameters of the evaluation metrics for both training and testing datasets. When evaluated on the testing datasets, it achieved an accuracy of 0.98, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.96 for identifying non-fertile eggs. The higher performance is attributed to its unique architecture, efficiently capturing features at different scales, which leads to improved accuracy and robustness. Further optimization and fine-tuning of the models might be necessary to address the limitations in accurately detecting fertile and non-fertile eggs using other models. This study highlighted the potential of CNN-transfer learning for nondestructive fertility detection and emphasized the need for further research to enhance the models’ capabilities and to ensure accurate *** Codes CS-Class: 68T07, 68T45, 68U10, ICT-Class: 94A08 © 2023, CC BY-SA.

关键词： Image classification

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Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency – A review and examples of quantitative measurement methods

Evaluating the Inclusiveness of Artificial Intelligence Soft...

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Artificial Intelligence, computer, Data Sciences and Applications (ACDSA), International Conference on

作者： Vasileios Alevizos Ilias Georgousis Akebu Simasiku Antonis Messinis Sotiria Karypidou Dimitra Malliarou Department of Learning Informatics Managament and Ethics Karolinska Institute Stockholm Sweden Department of Computer Science MLV Research Group International Hellenic University Kavala Greece Department Of Computer Software Engineering Zambia University Ndola Zambia HEDNO SA (Hellenic Electricity Distribution Network Operator) Department of Computer Science MLV Research Group International Hellenic University IntelliSolutions Athens Greece

The escalating integration of Artificial Intelligence (AI) in various domains, especially Project Management (PM), has brought to light the imperative need for inclusivity in AI systems. This paper investigates the role of AI software in augmenting both the inclusiveness and efficiency within the realm of PM. The research pivots around specific criteria that define and measure the inclusiveness of AI in PM, highlighting how AI, when developed with inclusiveness in mind, can significantly enhance project outcomes. However, there are inherent challenges in achieving this inclusiveness, primarily due to biases embedded in AI learning databases and the design and development processes of AI systems. The study offers a comprehensive examination of AI's potential to revolutionize PM by enabling managers to concentrate more on people-centric aspects of their work. This is achieved through AI’s ability to perform tasks such as data collection, reporting, and predictive analysis more consistently and efficiently than human counterparts. However, the incorporation of AI in PM extends beyond mere efficiency; it represents a paradigm shift in the epistemology of PM, calling for a deeper understanding of AI's role and impact on society. Despite these advantages, the adoption of AI comes with significant challenges, particularly in terms of bias and inclusiveness. Biased AI learning databases, which use shared and reusable datasets, often perpetuate initially discriminatory algorithms. Moreover, unconscious biases and stereotypes of AI designers, developers, and trainers can inadvertently influence the behavior of the AI systems they create. This necessitates a paradigmatic shift in how AI systems are developed and governed to ensure they do not replicate or exacerbate existing social inequalities. The research proposes a methodological approach involving the development of criteria for inclusion and exclusion, alongside data extraction, to evaluate the inclusiveness and efficien

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