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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface 1

Optical Evanescent Field for Direct Observation of Sub-100nm...

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1st International Conference on Robotics, engineering, Science, and technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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Anomaly Detection With Quantified Explanations for Multivariate Time Series Networking Data

Anomaly Detection With Quantified Explanations for Multivari...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Marantis, Panagiotis Ramantas, Kostas Verikoukis, Christos Iquadrat Informatica S.L Barcelona Spain University of Patras Dept. of Computer Engineering and Informatics Patras Greece Athena Research Center Athens Greece Industrial Systems Institute Patras Greece

ISBN: (纸本)9798350351255

Next-generation telecommunication networks provide vast amounts of Multivariate Time Series monitoring data during their operation. Using these data to automatically detect anomalous system behavior is of critical importance to operators and Internet Service Providers, in order to analyze the network state and take mitigating actions to resolve possible faults. For the output of this Anomaly Detection process to be trusted, comprehensive and accurate explanations must be provided. These explanations are essential to make critical decisions that affect the network state. In this paper, we present a framework that utilizes reconstruction-based Deep Neural Network models to perform the anomaly detection task, while also using an explainability module that provides explanations of the results. The quality of the explanations is quantified through the use of interpretation labels. In addition, we provide a comparative analysis, performing Anomaly Detection on three different open-source datasets from the mobile networking domain, while testing multiple Deep Learning models and comparing their performance. The presented results show that our framework accurately detects various types of anomalies that can be present in mobile networks, while offering the flexibility to identify and select the model that offers the best performance for each dataset, in both the Anomaly Detection and explainability tasks. © 2024 IEEE.

关键词： DNNs Explainable Anomaly Detection Machine Learning Multivariate Time Series Next-Generation Networks Reconstruction-based Models

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Evaluation of Pseudorandom Pixel Placement in Enhancing Face Recognition using VGG-Face 5

Evaluation of Pseudorandom Pixel Placement in Enhancing Face...

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5th International Conference on Image, Video Processing, and Artificial Intelligence, IVPAI 2023

作者： Hadis, Muhammad Sabirin Akita, Junichi Toda, Masashi Dept. of Electrical Engineering and Computer Science Kanazawa University Kakuma-machi Ishikawa Kanazawa920-1192 Japan Research and Education Institute for Semiconductors and Informatics Kumamoto University Kumamoto860-8555 Japan

ISBN: (纸本)9781510674660

In efforts to enhance face recognition performance, techniques ranging from super-resolution methods to the use of Local Binary Pattern (LBP) and deep learning have been explored. Among these, the pseudorandom pixel placement (PSE) technique has demonstrated potential in face recognition. Nevertheless, its testing was previously limited to just 8 subjects. This study undertakes a comprehensive evaluation of the PSE technique with a larger sample, utilizing 2000 subjects from the DigiFACE1M dataset and leveraging the state-of-the-art VGG-Face deep learning model. Through experiments involving 10 different PSE patterns on 144.000 face images, our findings indicate that, compared to Regular Pixel Placement (REG), PSE achieved an improvement in average accuracy by 1.05%, reduced the standard deviation by 1.47%, and resulted in 31 additional subjects achieving 100% accuracy. We conclude that PSE consistently outperforms REG in face recognition tasks using the VGG-Face model across the majority of tested scenarios. © 2024 SPIE. All rights reserved.

关键词： Pixels

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Optimizing 3D Flight Paths for Multiple UAVs with Connectivity Management in Urban Delivery Systems 19

Optimizing 3D Flight Paths for Multiple UAVs with Connectivi...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Lee, Yeongmin Syahran, Raihan Muhammad Spangenberger, Mael Yang, Huigyu Oh, Jiyong Choo, Hyunseung Dept. of Artificial Intelligence Sungkyunkwan University Suwon Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon Korea Republic of Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Informatics Télécom SudParis Évry France

ISBN: (纸本)9798331507817

The dynamic deployment of aerial vehicles in urban delivery scenarios demands precise route planning, reliable data links, and efficient use of network infrastructure. Although prior efforts have explored various aspects of unmanned navigation or communication optimization, existing approaches often overlook the combined impact of three-dimensional path selection and robust connectivity maintenance. To address this gap, this paper proposes a deep reinforcement learning framework employing Independent Proximal Policy Optimization (IPPO) to steer the flight paths of unmanned vehicles while curtailing needless interactions with base stations (BSs). By incorporating communication-specific metrics, Reference Signal Received Power (RSRP), the proposed system adapts to urban conditions and upholds resilient network links. Comprehensive performance evaluations reveal the potential of the framework to reduce excessive BS connections by up to 82.24%, preserve RSRP levels above -80 dBm, and balance handover frequencies across flight trajectories. These findings affirm the scalability and effectiveness of the method for achieving efficient aerial navigation and consistent communication in urban delivery services. © 2025 IEEE.

关键词： Deep reinforcement learning

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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Automated AI-Driven Detection of Mastoiditis in Temporal Bone via CT Scan and MRI Imaging: A Review 6

Automated AI-Driven Detection of Mastoiditis in Temporal Bon...

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6th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2025

作者： Bhole, Chitra Nemade, Chaitanya Shetty, Chandrakant Patil, Atharv Yadav, Pranav Dept. Computer Engineering K.J. Somaiya Institute of Technology Sion Mumbai India Department K.J. Somaiya Medical College & Research Center Sion Mumbai India

ISBN: (纸本)9798331522667

Recent advances in using artificial intelligence (AI) in medical imaging toward the detection of mastoiditis have shown very promising applications. Therefore, the comprehensive review on the state-of-art approaches related to the application of CT and MRI imaging modalities for automated mastoiditis detection is provided. We review methodologies that go from conventional machine learning approaches such as SVM and Random Forests, to more novel and advanced deep learning architectures, such as U-Net, ResNet, or ViT, to image temporal bones. The review systematically assesses particular techniques: transfer learning using pre-trained models, attention mechanisms focusing feature extraction, and multi-modal fusion approaches combining the data found in CT and MRI. Window level adjustments, skull stripping, adaptive histogram equalization are subjected to critical review regarding the pre-processing methods. Its review ranges over performance metrics across the studies;the current systems achieve sensitivity rates of 85-95% and specificity of 88-97% within the controlled environment. We discuss some of the key challenges such as limited availability of dataset, variance in imaging protocols, and the need for robust clinical validation. Recent evidence from AI-driven systems is promising in the support of the process for clinical diagnosis, especially when using ensemble approaches. There could be several architectures that would work in combination to enhance the robustness and efficacy of clinical diagnosis. © 2025 IEEE.

关键词： computerized tomography

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RadioModRec-l: A Large Scale Radio Frequency Dataset for Automatic Modulation Recognition research 4

RadioModRec-l: A Large Scale Radio Frequency Dataset for Aut...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adetiba, Emmanuel Olasina, Jamiu R. Obiyemi, Obiseye Covenant University Covenant Applied Informatics and Communication Africa Center of Excellence Dept. of Electrical and Information Engineering Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban4001 South Africa Federal Polytechnic Ilaro Dept. of Computer Engineering Ilaro Nigeria Durban University of Technology Space Science Centre Dept. of Electrical Power Engineering Durban South Africa

ISBN: (纸本)9798350353266

Access to well-curated large datasets remains a significant bottleneck in AI-based research within wireless communication. Rapid advancements in neighbouring fields, such as computer vision and natural language processing, are largely due to the availability of extensive open-access datasets. However, similar progress has not been observed in wireless communication. To address this gap, we curated a comprehensive dataset for fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK. Our dataset considers Rayleigh and Rician channel models under Additive White Gaussian Noise (A WGN) with SNRs ranging from -20dB to + 20dB in 5dB increments. The data samples were converted to constellation signal images and carefully pre-processed. Named RadioModRec, this dataset provides a valuable resource for researchers to train and evaluate AI models. It is freely accessible on Kaggle, promoting further innovation in the wireless communication domain. © 2024 IEEE.

关键词： Signal to noise ratio

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Analysing and Modelling the Accuracy and Latency Trade-offs in Rate Limiting on API-Gateway

Analysing and Modelling the Accuracy and Latency Trade-offs ...

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International research Conference on Smart Computing and Systems engineering (SCSE)

作者： Krishnamoorthy Caucidheesan Guhanathan Poravi Dept. of Computer Science and Engineering University of Westminster London UK Dept. of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331523268

ISBN: (纸本)9798331523275

The rate limiting service in API gateways controls request entry by throttling requests with a boundary. The rate limiting accuracy determines how efficiently it works and whether it allows requests within the throttle count or exceeds the throttle count. Latency, on the other hand, is the round-trip time of a particular API call. The accuracy of rate limiting service is defined using spillover error percentage. It is the error calculation for the requests that rate limiting service allows more than the throttle count. Requests must be sent to the rate limiting service in order to rate limit requests in the API gateway. The rate limiting service decides whether the incoming requests must be throttled. The time taken for the requests to be decided by the rate limiting service adds additional latency to the round-trip time of requests. This additional latency can be controlled by introducing a timeout. However, this could result in a degradation in the accuracy of the rate limiting. This paper investigates the particular problem and models the relationship between accuracy and round-trip latency. The findings of this research address the analysis of the accuracy and latency trade-off with respect to the parameters influencing them, and also address the prediction outcome using random forest regressor and present key findings.

关键词： Hands Degradation Analytical models Limiting Accuracy Logic gates Predictive models Low latency communication

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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques

Sensitive Content Detection in Social Networks Using Deep Le...

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Big Data, Cloud Computing, Data Science & engineering (BCD)

作者： Isidoros Perikos Computer Engineering and Informatics Department University of Patras Patras Greece Electrical and Computer Engineering Dept. Computer Technology Institute and Press “Diophantus” University of Peloponnese Patras Greece

ISBN: (数字)9798350394191

ISBN: (纸本)9798350394207

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience.

关键词： Deep learning Privacy Sensitivity Accuracy Social networking (online) Decision making Predictive models Transformers User experience Cultural differences

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Visual Field Prediction for Fundus Image with Generative AI 18

Visual Field Prediction for Fundus Image with Generative AI

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kang, Honggu Ko, Seonghyeon Kim, Juchan Le, Duc-Tai Bum, Junghyun Han, Jongchul Choo, Hyunseung Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Suwon Korea Republic of Dept. of Ophthalmology Samsung Medical Center Seoul Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798350331011

Accurate diagnosis of glaucoma is crucial due to its high risk of blindness, but the long examination time often undermines the reliability of results by examinee's subjective factors. We propose a method to reduce examination time by generating static perimetry results with Conventional Fundus Images (CFIs) utilizing the CFI2GM technique, which leverages multimodal data. Based on data from 3,306 glaucoma patients at Samsung Medical Center in Seoul, we conducted ophthalmic image translation utilizing the Pix2Pix model. Our method, combining cGAN, L1, and SSIM loss, achieved MSE 57.9886 and PSNR 30.6057 dB. Furthermore, we received positive feedback from ophthalmologist regarding the high practical applicability of the images generated by our method. This indicates that CFI2GM can enhance the reliability of glaucoma examination results as well as reduce testing time. © 2024 IEEE.

关键词： Diagnosis

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