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2023 Research in Adaptive and Convergent Systems, RACS 2023

作者： Kim, Soohyun Koo, Kyoyeong Park, Taeyong Lee, Jeongjin Department of Computer Science and Engineering Soongsil University Seoul Korea Republic of Department of Biomedical Informatics Hallym University Medical Center Gyeonggi-do Korea Republic of School of Computer Science and Engineering Soongsil University Seoul Korea Republic of

ISBN: (纸本)9798400702280

To improve1 the survival rate of hepatocellular carcinoma (HCC), early diagnosis and treatment are essential. Early diagnosis HCC often involves comparing and analyzing hundreds of computed tomography (CT) images, which is a kind of subjective judgment and is also a time-consuming process. In this paper, we propose a liver rigid registration method using liver and liver vessel surface mesh to enable fast and objective diagnosis of HCC. The proposed method involves segmenting the liver and liver vessel regions from abdominal CT images, generating surface meshes, and performing liver rigid registration based on the Iterative Closest Point (ICP) algorithm using the generated meshes. We evaluate the accuracy of the proposed method through experiments, demonstrating its potential for rapid and objective diagnosis of HCC. The performance evaluations show that the proposed method aids in the early diagnosis and treatment of HCC fast and objectively. © 2023 ACM.

关键词： Medical imaging

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Normative Modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s Disease Diagnosis and Biomarker Identification 3rd

Normative Modeling with Focal Loss and Adversarial Autoenc...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Zhao, Songlin Zhou, Rong Zhang, Yu Chen, Yong He, Lifang Department of Computer Science and Engineering Lehigh University BethlehemPA United States Department of Bioengineering Lehigh University BethlehemPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States

ISBN: (纸本)9783031820069

In this paper, we introduce a novel normative modeling approach that incorporates focal loss and adversarial autoencoders (FAAE) for Alzheimer’s Disease (AD) diagnosis and biomarker identification. Our method is an end-to-end approach that embeds an adversarial focal loss discriminator within the autoencoder structure, specifically designed to effectively target and capture more complex and challenging cases. We first use the enhanced autoencoder to create a normative model based on data from healthy control (HC) individuals. We then apply this model to estimate total and regional neuroanatomical deviation in AD patients. Through extensive experiments on the OASIS-3 and ADNI datasets, our approach significantly outperforms previous state-of-the-art methods. This advancement not only streamlines the detection process but also provides a greater insight into the biomarker potential for AD. Our code can be found at https://***/soz223/FAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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A Hierarchical Reinforcement Learning Approach for Adaptive Quadruped Locomotion of a Rat Robot

A Hierarchical Reinforcement Learning Approach for Adaptive ...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Zhang, Zitao Huang, Yuhong Zhao, Zijian Bing, Zhenshan Knoll, Alois Huang, Kai Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Technical University of Munich Department of Informatics Munich Germany

ISBN: (纸本)9798350325706

Small robots encounter considerable difficulties in learning effective motions on complex terrains owing to their underactuated nature and nonlinear dynamics. In this paper, we present a novel approach for robot motion generation that implements reinforcement learning, based on simplified exploration of the robot's action and time slice conduction. Our approach controls the robot's actions using normalized signals and hierarchical mappings on mathematical space, which facilitates the learning process. We execute action in the timeslice to make efficient interaction with the environment. The effectiveness of our methodology is evaluated across a diverse range of simulated terrain scenarios, supplemented by physics validation. Our results show that our approach performs effective on complex terrains that are designed for small-sized robots. © 2023 IEEE.

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DDoS Attack Detection and Mitigation in Software-Defined Networking-Based 5G Mobile Networks with Multiple Controllers 9

DDoS Attack Detection and Mitigation in Software-Defined Net...

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9th International Conference on Future Internet of Things and Cloud, FiCloud 2022

作者： Sheibani, Morteza Konur, Savas Awan, Irfan University of Bradford Faculty of Engineering and Informatics Department of Computer Science Bradford United Kingdom

ISBN: (数字)9781665493505

ISBN: (纸本)9781665493505

The increased risk of Distributed Denial-of Service (DDoS) attacks is one of the crucial security issues for 5G networks. Software defined networking (SDN) is one of the key solutions for the centralised control of network behavior for detection and mitigation of network attacks. Single centralised controllers for the SDN-based 5G networks lead to many problems concerning scalability, reliability and other system-related performance. In this respect, employing dispersed controllers will provide a vital solution. However, there are still some issues such as how to equalise the processing burden among these controllers and how to employ multi-controllers to improve the security aspects of the SDN-based 5G networks. In this paper, we propose a new method to equalise the processing burden among the dispersed controllers in SDN-based 5G networks in a dynamic manner. The proposed system employs two levels of controllers: dispersed controllers and a master controller. The master controller manages and performs the grouping of the dispersed controllers. The decision of the groups' reconstruction is the responsibility of the master controller and it is based on the processing burden of the dispersed controllers. The proposed dispersed algorithm is employed in 5G networks to detect and mitigate the DDoS attacks. The simulation results prove that the proposed system performs well in equalising the processing burden among controllers and detection and mitigation of DDoS attacks. © 2022 IEEE.

关键词： Controllers

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Mask Detection Using Convolutional Neural Network

Mask Detection Using Convolutional Neural Network

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2022 International Seminar on Application for Technology of Information and Communication, iSemantic 2022

作者： Abdussalam Pramudya, Elkaf Rachmawan Setiono, Oki Muslih, Muslih Dian Nuswantoro University Faculty of Computer Science Department of Informatics Engineering Semarang Indonesia

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

COVID-19 virus has hit Indonesia since early March 2020. One of the government's efforts to prevent the spread of COVID-19 is to do physical distancing to require people to wear masks when doing activities outside the home. One way to overcome this problem is by detecting mask users to be more obedient and obedient to the rules, then the identification process is carried out for mask users and those who do not use masks. The process is carried out using the Convolutional Neural Network method. CNN is known to be superior and does not require pre-processing so it saves more time. In terms of algorithmic competence, CNN is considered capable of carrying out the data detection process well. Of the 1376 datasets used, 30 epochs, accuracy = 0.988, recall = 0.990, precision = 0.987, and F1 = 0.988 with the required detection time for each image between 4 to 5 seconds. © 2022 IEEE.

关键词： Convolutional neural networks

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Applying Soft System Methodology for a clearer understanding of the future Intensive Care Units 23

Applying Soft System Methodology for a clearer understanding...

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Proceedings of the 27th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Dimitrios Markopoulos Anastasios Tsolakidis Nikitas Karanikolas Aikaterini Marinagi Christos Skourlas Department of Informatics and Computer Engineering University Of West Attica Greece Department of Informatics and Computer Engineering University of West Attica Greece Agricultural University of Athens Greece

ISBN: (纸本)9798400716263

Intensive Care Units (ICUs) generate a large amount of valuable data related to the health status of patients. In addition, ICUs can leverage other sources of big data, such as structured data, text, video, and images. In this work, a framework for the future ICU system is proposed, which is based on the Soft System methodology (SSM) and the use of big data technology. The framework and the related activity models ensure that the ICU can have its particularities and specialties, as well as its core services and functions. The application of the framework also implies that ICU can provide ongoing expertise and training to upgrade its staff, can improve interoperability with the National Health System, and ICU staff intercommunication and remote services.

关键词： Big data Intensive Care MIMIC III Soft System Methodology

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A Novel Approach to Visual Search in E-commerce Fashion Using Siamese Neural Network and Multi-Scale CNN 25

A Novel Approach to Visual Search in E-commerce Fashion Usin...

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25th International Electronics Symposium, IES 2023

作者： Ma'Rufah, Laila Karlita, Tita Sa'Adah, Umi Fauzi, Willy Achmat Politeknik Elektronika Negeri Surabaya Informatics and Computer Engineering Department Surabaya Indonesia PT. Sinergi Dimensi Informatika Surabaya Indonesia

ISBN: (纸本)9798350314731

The growing number of users on fashion e-retail platforms has highlighted the limitations of the current text-based search system, as users struggle to effectively describe the clothing products they are searching for. To address this challenge, the integration of a visual search system becomes essential. Visual search allows users to find similar products based solely on images. In this research, we propose the utilization of a Siamese Neural Network for implementing visual search in an e-commerce fashion. The Siamese network consists of two identical networks that process different images. By training the network to learn and represent the differences between images as embedding vectors, we enable the generation of similarity scores for pairs of images. To achieve accurate feature extraction, we employ a Multi-Scale CNN approach that captures both high and low-level features. Various experiments are conducted in this study, including the selection of multi-scale CNNs, distance metrics, and diverse data, to determine the network architecture that achieves the highest accuracy. Our findings demonstrate that utilizing the VGG19 model with a shallow layer and fractional distance metrics achieves an impressive accuracy of up to 97%. This indicates the effectiveness of our model in accurately predicting image similarity. © 2023 IEEE.

关键词： Electronic commerce

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Key Lessons from Microservices for Data Mesh Adoption 3

Key Lessons from Microservices for Data Mesh Adoption

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Ashraf, Abdulrahman Hassan, Ahmed Mahdi, Hani Ain Shams University Computer and Systems Engineering Department Cairo Egypt Egypt University of Informatics Ain Shams University Cairo Egypt

ISBN: (纸本)9798350306231

Data Mesh constitutes an architectural blueprint tailored to facilitate the execution of enterprise data platforms within expansive and intricate organizations. Its design aims to propel the broad adoption of analytics beyond a single platform and solitary implementation team. This research delves into the convergence of Microservices and Data Mesh structures, pinpointing vital learnings from Microservices that can inform and ease the transition to Data Mesh architecture. Several critical components like Domain-Driven Design, data consistency, team autonomy, technology selection, and scalability are included in this investigation, each with significant implications for the successful implementation of a Data Mesh. © 2023 IEEE.

关键词： Software engineering

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The Basics of Neutrosophic Simulation for Converting Random Numbers Associated with a Uniform Probability Distribution into Random Variables Follow an Exponential Distribution

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Neutrosophic Sets and Systems 2023年 53卷 358-366页

作者： Jdid, Maissam Alhabib, Rafif Salama, A.A. Faculty of Informatics Engineering Al-Sham Private University Damascus Syria Department of Mathematical Statistics Faculty of Science Albaath University Homs Syria Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt

When performing the simulation process, we encounter many systems that do not follow by their nature the uniform distribution adopted in the process of generating the random numbers necessary for the simulation process. Therefore, it was necessary to find a mechanism to convert the random numbers that follow the regular distribution over the period [0, 1] to random variables that follow the probability distribution that works on the system to be simulated. In classical logic, we use many techniques in the transformation process that results in random variables that follow irregular probability distributions. In this research, we used the inverse transformation technique, which is one of the most widely used techniques, especially for the probability distributions for which the inverse function of the cumulative distribution function can found. We applied this technique to generate neutrosophic random variables that follow an exponential distribution or a neutrosophic exponential distribution. This based on classical or neutrosophic random numbers that follow a regular distribution. We distinguished three cases according to the logic that each of the random numbers or the exponential distribution follows. We arrived at neutrosophic random variables that, when we use them in systems that operate according to an exponential distribution, such as queues and others, will provide us with a high degree of accuracy of results, and the reason for this is due to the indeterminacy provided by neutrosophic logic © 2023, Neutrosophic Sets and *** Rights Reserved.

关键词： Random variables

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AVIG: A Real-Time Visual Inspection for Guava Grading System Using computer Vision and XGBoost 23

AVIG: A Real-Time Visual Inspection for Guava Grading System...

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8th International Conference on Sustainable Information engineering and Technology, SIET 2023

作者： Setyawan, Raden Arief Perdana, Rizal Setya Harilegawa, Made Wena Balantimuhe, Alan Stevrie Basuki, Achmad Department of Electrical Engineering Faculty of Engineering Brawijaya University Malang Indonesia Department of Informatics Engineering Faculty of Computer Science Brawijaya University Malang Indonesia Directorate of Information Technology Brawijaya University Malang Indonesia

ISBN: (纸本)9798400708503

The conventional approach to manual inspection can be described by its considerable time and labor demands and the unavoidable occurrence of human error when faced with the inspection of a large number of products. Therefore, the application of an automated visual inspection system is frequently employed. The camera(s) can easily capture visual features for automatic visual inspection. However, it is essential to note that there are limitations when using this method to estimate the size-based grading system. The volume measurement is an essential feature in the field of automatic image analysis for fruit and vegetable grading, specifically guava. In this study, we propose a novel image analysis approach that utilizes a dual-view camera system to enhance the accuracy of volume approximation, particularly in scenarios involving non-perfect spherical objects. Hence, this research presents the training process of a comprehensive model for estimating the volume of objects with irregular shapes, named AVIG (Automatic Visual Inspection for Grading). The model utilizes the ellipsoid approximation technique, a dual-view camera, and XGBoost algorithm. The results from the accuracy comparison demonstrate that XGBoost shows the highest degree of accuracy, specifically 86%, thereby outperforming the performance of other algorithms. The comprehensive experiment conducted in this study has shown that the proposed architecture, AVIG, exhibits superior performance compared to previous research in terms of automatic evaluation of moving objects. © 2023 ACM.

关键词： computer vision

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