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Towards a Functional Continuum Operating System - ICOS MetaOS 1st

Towards a Functional Continuum Operating System - ICOS MetaO...

引用

1st Global Internet of Things and Edge Computing Summit

作者： Jaworski, Artur Kotlinski, Marcin Zrazinska, Izabela Masip-Bruin, Xavier Garcia, Jordi Esfandiyar, Iman Poznan Supercomp & Networking Ctr Jana Pawla II 10st PL-61139 Poznan Poland Worldsensing Viriat 47 10th Floor Barcelona 08014 Spain UPC BarcelonaTECH CRAAX Lab Rambla Exposicio 59 Vilanova I La Geltru 08800 Spain Poznan Inst Technol Lukasiewicz Res Network Ewarysta Estkowskiego 6 st PL-61755 Poznan Poland

ISBN: (纸本)9783031785719;9783031785726

This article presents the intermediate results of the ICOS project, which aims to create a meta-operating system for cloud continuum, and describes the adaption of ICOS in two (out of four) project's pilot Use Cases. For each described scenario, the paper provides insight into the specific key architectural features, identifies the expected benefits, and provides some initial validation results. The purpose of this research is to highlight the advantages of using a metaOS in real scenarios as well as to illustrate the ease of integration into ICOS.

关键词： ICOS Continuum MetaOS Internet of Things

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A Concept of Postal Tracking Solution 4th

A Concept of Postal Tracking Solution

引用

4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Mekhaznia, T. Ghabban, Fahad Bradji, R. Ounis, C. Zerfaoui, B. Univ Cheikh Larbi Tebessi LAMIS Lab Tebessa Algeria Taibah Univ Coll Comp Sci & Engn Medina 41477 Saudi Arabia

ISBN: (纸本)9783031821493;9783031821509

The postal tracking concept is dedicated to override the prevailing issues occurred within manual system. It supported to eliminate issues that can reduce hardship especially when smooth and effective operations are needed. The aim of the paper is to summarize and diagnosis of most popular existent systems through their technical structure and use by revealing their issues. A comparative study is also presented in order to endorse the conception of new related approaches in terms of effectiveness and security.

关键词： PTS PIV PIN EPS

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Diabetic Retinopathy Grade Assessment Using ResNetRS 4th

Diabetic Retinopathy Grade Assessment Using ResNetRS

引用

4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Bouslimi, Yahia Bouslimi, Dhia Elhak Echi, Afef Kacem Ben Ayed, Leila Univ Tunis LaTICE ENSIT 5 Ave Taha Hussein Bab Mnara Tunis Tunisia Univ Manouba ENSI R377 FGR Campus Univ Manouba Manouba 2010 Tunisia

ISBN: (纸本)9783031821523;9783031821530

Early identification and grading of Diabetic Retinopathy (DR) is crucial to prevent vision loss. In this study, we used a large dataset of pre-processed retinal scans to assess the performance of deep learning models in achieving this goal. The dataset was processed by combining, enhancing, resizing, cleaning, balancing, and applying Gaussian filtering to retinal images from APTOS 2019 Blindness Detection and DRD. We focused on the ResNetRS models, specifically ResNetRS101, which has been shown to perform exceptionally well in medical imaging tasks, making it a promising tool for DR analysis. Our findings indicate that ResNetRS has excellent feature learning capabilities that can identify subtle indicators that are crucial for diagnosing the condition. Additionally, its deep architecture and residual connections ensure efficient gradient flow, reduce overfitting, and enhance performance across diverse datasets. Our proposed model achieved a training accuracy of 98.9% and a test accuracy of 93% which compete with the related works.

关键词： Diabetic Retinopathy Detection Severity Grading ResNetRS101

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JaxDecompiler: Redefining Gradient-Informed Software Design 7th

JaxDecompiler: Redefining Gradient-Informed Software Design

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7th International Conference on Optimization and Learning

作者： Pochelu, Pierrick Univ Luxembourg FSTM DCS Esch Sur Alzette Luxembourg

ISBN: (纸本)9783031779404;9783031779411

Among numerical libraries capable of computing gradient descent optimization, JAX stands out by offering more features, accelerated by an intermediate representation known as Jaxpr language. However, editing the Jaxpr code is not directly possible. This article introduces JaxDecompiler, a tool that transforms any JAX function into an editable Python code, especially useful for editing the JAX function generated by the gradient function. JaxDecompiler simplifies the processes of reverse engineering, understanding, customizing, and interoperability of software developed by JAX. We highlight its capabilities, emphasize its practical applications especially in deep learning and more generally gradient-informed software, and demonstrate that the decompiled code speed performance is similar to the original.

关键词： Gradient Descent Software Design Decompilation Deep Learning

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A Deep Learning-Based Method Facilitates scRNA-seq Cell Type Identification 5th

A Deep Learning-Based Method Facilitates scRNA-seq Cell Type...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Wang, Xin Li, Zhuo Hang, Jie Xu, Ren Meng, Lin Ritsumeikan Univ Grad Sch Sci & Engn Kusatsu 5250058 Japan Xiamen Univ Sch Med State Key Lab Cellular Stress Biol Xiamen 361102 Peoples R China Ritsumeikan Univ Coll Sci & Engn Kusatsu 5250058 Japan

ISBN: (纸本)9789819770007;9789819770014

The repetitive analysis of similar scRNA-seq samples for cell identification is both time-consuming and labor-intensive. Transferring annotations from a reference to a query dataset offers innovative strategies for single-cell annotation, with deep learning's prowess in feature extraction serving as a robust foundation for these approaches. In this paper, we present the Multi-level Feature Extractor-attention model (MLFE-Att), a deep learning framework that leverages the multi-head attention mechanism to learn intricate cellular correlation features, thereby enhancing the identification of cell categories with minor differences. Deeper and non-linear features of cells are learned by the module that contains multiple fully connected layers and activation layers. Experimental evaluations on the bone marrow scRNA-seq dataset demonstrate that MLFE-Att outperforms the other two baseline models, MLFE-CNN and MLFE-LSTM, with a 99.84% identification accuracy. The results on the new subset of the bone marrow dataset indicate that MLFE-Att offers dependable support for identifying cells in the new sample, achieving a 92.76% accuracy, and supplies insightful feedback for manual cellular labeling.

关键词： scRNA-seq Deep learning Cell identification

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An Anomaly-Misuse Hybrid System for Efficient Intrusion Detection in Clustered Wireless Sensor Network Using Neural Network 5th

An Anomaly-Misuse Hybrid System for Efficient Intrusion Dete...

引用

5th International Conference on Computing science, Communication and Security

作者： Nathiya, N. Rajan, C. Geetha, K. Dinesh, S. Aruna, S. Brinda, B. M. Paavai Coll Engn Namakkal Tamilnadu India KSR Coll Technol Thiruchengode Tamilnadu India Excel Engn Coll Komarapalayam Tamilnadu India

ISBN: (纸本)9783031751691;9783031751707

Wireless Sensor Networks (WSN) refer to a group of small self-sustaining processor-based systems which collect information from their sensors, produce a computation set, and data relayed to a Base Station. The nodes deployment is done over a range of environment types extending from harsh to hostile. The network's requirements vary depending on the environment type. WSNs must have self-sufficiency and autonomy in harsh environments. Whilst, security is crucial in hostile environments, where the WSNs must be trustworthy and secure. In order to reduce production costs and decrease power usage, the design of nodes in WSNs is typically very simple. Sensor networks inherit all aspects of WSNs but also have their own unique features. Thus, the WSN security model design is quite distinctive from that of Ad hoc networks. In hostile environments, an Intrusion Detection System (IDS) is very vital for WSNs as it has the ability to identify malicious network packets. IDS can be efficiently employed in numerous methods like Neural Networks. Despite that, the classification algorithms must have the least cost of computation in resource-constrained environments. This work has proposed a novel clustering algorithm with an integrated IDS classifier using the modified Neural Network. The Neural Network structure can be optimized by the proposed System Mentoring-Learning-Based technique for detection of optimal cluster-heads, and enhancement of the intrusions' classification accuracy.

关键词： Wireless Sensor Networks (WSN) Intrusion Detection System (IDS) computational cost clustering algorithm with integrated IDS classifier using modified Neural Network and System Mentoring-Learning-Based technique

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Tourists' Intention to Revisit World Cultural Heritage Sites: An Evidence from Hue City, Vietnam 5th

Tourists' Intention to Revisit World Cultural Heritage Sites...

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5th 5th Olympus - International Conference on Supply Chain

作者： An, Le Thanh Hung, Vo Viet Hue Univ Univ Econ Hue City 530000 Vietnam

ISBN: (纸本)9783031693502;9783031693519

This study aims to examine the causal relationship between constructs by focusing on the effects of heritage destination image, tourist motivation, experience, and satisfaction on revisit intention. Analyzed through the use of partial least squares structural equation modeling (PLS-SEM), the data was collected from 361 tourists who visited heritage sites in Hue City, a world cultural heritage tourism destination in Central Vietnam. The results indicated that heritage destination image, tourist experience, and satisfaction had a significantly positive and direct effect on tourists' intentions to revisit heritage sites. Among these factors, tourism experience was considered a key determinant of revisit intention. The findings of this study revealed that tourist experience and satisfaction are key moderators between destination image and revisit intention in most of the relationships, contributing to a better understanding of tourists' intentions to revisit cultural heritage tourism destinations.

关键词： Destination image Tourist experience Tourist motivation Revisit intention Tourist satisfaction PLS-SEM Hue City Vietnam

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A Novel Elm Optimization and Its Application in IoT Intrusion Detection 5th

A Novel Elm Optimization and Its Application in IoT Intrusio...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Maseno, Elijah M. Wang, Zenghui Univ South Africa Dept Comp Sci Florida South Africa Univ South Africa Dept Elect & Smart Syst Engn ZA-1709 Florida South Africa

ISBN: (纸本)9789819770038;9789819770045

Globally, the Internet of Things (IoT) has grown exponentially in the last few years. The integration of numerous IoT devices enhances connectivity but also exposes them to various cyber-attacks. With the increase of complex and intelligent cyber-attacks, the traditional methods of cyber-attack detection, such as anomaly and misuse intrusion detection systems (IDS), have proven ineffective in combating emerging cyber-attacks. Scholars have suggested that the existing IDS's performance can be enhanced by integrating machine learning methods. This study explored the integration of ELM (Extreme Learning Machine) with NSGA-II (Non-Dominated Sorting Genetic Algorithm II). Our empirical analysis reveals that ELM outperforms other algorithms regarding learning rate, usability, and effectiveness of generalization. NSGA-II provides an equilibrium between exploration and exploitation, successfully managing the trade-offs present in multi-objective optimization problems to identify a range of excellent solutions along the Pareto front. Most of the existing works on ELM optimization focuses on improving one input parameter. This study explored the integration of ELM with NSGA-II for both input weights and hidden neurons. The model was trained and tested using two datasets namely IoT_ToN network and UNSWNB15 datasets. This research used TON_IoT Windows dataset to further test the capacity of the model to identify novel attacks. Using the UNSWNB15 and IoT_ToN network datasets, respectively, the model's accuracy was 0.68 and 0.65. In addition, the model recorded an accuracy 0.768 using TON_IoTWindows dataset, demonstrating the potential of the model in detection of new forms of attacks. This study acknowledges the ongoing evolution of challenges in IoT security and the need for continuous innovation to stay ahead of emerging threats in the future. The contributions of this study can be explored further to develop reliable intrusion detection solutions for the dynamic IoT landscape

关键词： Artificial Intelligence (AI) Internet of Things (IoT) Cyber-Attack Intrusion Detection System (IDS) Evolutionary Algorithm Optimization Machine Learning

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From Likes to Comments: Tracking Interaction Trends on Social Networks to Promote Cultural Events

From Likes to Comments: Tracking Interaction Trends on Socia...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Ferreira, Jessica Scalabrini, Elaine Lopes, Isabel Pereira, Joao Paulo Carfim, Sofia Inst Politecn Braganca UNIAG Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Campus Santa Apolonia P-5300253 Braganca Portugal

ISBN: (纸本)9783031834349;9783031834356

Tourism drives the economy through job creation and infrastructure development, while the cultural component, such as gastronomy and historical heritage, enriches the visitor experience and reduces regional disparities. Municipalities use channels like Instagram to promote cultural events and strengthen local identity, reaching a diverse audience and encouraging community and visitor engagement. This study explores interaction trends in event posts on Braganca, Portuguese City Municipality's Instagram from 2020 to 2023. The aim is to analyse the evolution of metrics such as likes, comments and views over the last four years. Using a quantitative approach, data was collected from Instagram posts using the APIFY platform, followed by an analysis using Python for statistical evaluation to identify correlations and patterns. The main results reveal significant variations in user engagement with different types of events and changes in follower behaviour over time. These findings offer valuable information for social media management and municipal event planning, emphasising the importance of adaptive strategies on digital platforms.

关键词： Smart Destinations Digital Transformation Social Media Interaction

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Fault Detection in Semiconductor Manufacturing Using Advanced Machine Learning Techniques with Interpretability 4th

Fault Detection in Semiconductor Manufacturing Using Advance...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Srisailam, Swaroop Mandal, Pallab Aditya, Kalur Lalith Pankajakshan, Praveen Rajamani, Kumar T. CropIn Technol Bangalore Karnataka India Indian Inst Technol Kanpur Kanpur Uttar Pradesh India Sri Sathya Sai Inst Higher Learning Anantapur Andhra Pradesh India

ISBN: (纸本)9783031837951;9783031837968

Fault detection in semiconductor manufacturing is complex due to class imbalance, missing values, outliers, and high data dimensionality. This study employs advanced machine learning techniques on the SECOM dataset to enhance fault detection accuracy and interpretability. Using oversampling methods like SMOTE and ADASYN, along with rigorous data cleaning and feature selection, we reduced features from 590 to 33. Principal Component Analysis (PCA) retained 91% of the dataset's variance. Several classifiers, including Random Forest, SVM, KNN, Logistic Regression, Gradient Boosting, and XGBoost, were employed. A Voting Classifier combining top-performing models achieved superior results, with an accuracy of 99.68%. Emphasizing interpretability, we applied Permutation Feature Importance (PFI) and Partial Dependence Plots (PDP) to understand the model's behavior and the significance of different sensors. Our approach offers a robust, accurate, and interpretable solution for fault detection in semiconductor manufacturing.

关键词： Semiconductor Manufacturing Machine Learning Data Preprocessing Interpretable Machine Learning

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