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Interpretable Liver Fibrosis Classification Using 1D-CNN 4th

Interpretable Liver Fibrosis Classification Using 1D-CNN

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

作者： Krishnan, S. Navaneetha Jothi, R. Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India

ISBN: (纸本)9783031837890;9783031837906

Liver fibrosis, characterized by the excessive accumulation of extracellular matrix proteins, represents a significant global health concern with the potential to progress to cirrhosis and hepatocellular carcinoma if left untreated. Early and accurate diagnosis of liver fibrosis is critical for effective management and treatment, yet traditional diagnostic methods, such as liver biopsy, are invasive, costly, and carry inherent risks. This necessitates the development of non-invasive, reliable, and efficient diagnostic tools. This study explores the application of machine learning techniques for classifying liver fibrosis using the Indian Liver Patient Dataset (ILPD). The dataset underwent rigorous preprocessing, including imputation, transformation, and resampling, ensuring robust model training. Several algorithms, including Random Forest Gradient Boosting, XGBoost, Bagging classifier, ExtraTrees Classifier, Stacking Classifier, KNeighbors Classifier, Artificial Neural Networks (ANN), and a 1D Convolutional Neural Network (1D-CNN) were evaluated. The 1D-CNN achieved the highest accuracy of 97.14%, followed by the Stacking Classifier with 96.64%. SHAP (SHapley Additive exPlanations) has been used to interpret the 1D-CNN, providing insights into feature importance. The findings demonstrate the potential of machine learning for non-invasive, efficient, and reliable liver fibrosis diagnosis.

关键词： Liver Fibrosis Deep learning CNN XAI SHAP

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Enhancing EEG-Based User Verification with a Normalized Neural Network Ensemble Approach 1

引用

8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Saia, Roberto Balia, Riccardo Podda, Alessandro Sebastian Pompianu, Livio Carta, Salvatore Pisu, Alessia Univ Cagliari Dept Math & Comp Sci Palazzo SciVia Osped 72 I-09124 Cagliari Italy

ISBN: (数字)9783031826337

ISBN: (纸本)9783031826320;9783031826337

The development of user identity verification approaches using biometric systems based on EEG data holds significant promise across various domains. However, the inherent complexity and variability of this data make designing reliable solutions challenging. In response to these challenges, this work introduces a Normalized Neural Network Ensemble (NNNE) approach for EEG-based user verification. It leverages neural networks to enhance the current state-of-the-art performance, aiming to overcome the problems associated with EEG data by capturing spatial and temporal patterns in EEG signals more effectively. In detail, the proposed approach relies on an architecture centered around an ensemble of Multi-Layer Perceptron artificial neural networks regulated by a soft voting criterion. As part of the preprocessing steps, the input data is normalized by transforming features based on quantile information. Additionally, the MLP hyperparameters and the number of MLP evaluators in the ensemble are automatically optimized. Considering the high heterogeneity of the state-of-the-art works in this field, which are characterized by a wide variability in the choices of components, approaches, and strategies, making comparisons between their performances difficult and sometimes impossible, this paper exploits the opportunity offered by the Biometric EEG Dataset (BED), which provides benchmark values that facilitate comparisons within the context of widely adopted approaches in literature in terms of stimuli and feature extraction techniques. The experimental results show that the proposed NNNE approach improves the performance of the state-of-the-art one (Hidden Markov Model) used by the authors of the dataset to define the reference values, significantly.

关键词： AI security Access control Biometrics Verification EEG Stimuli

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A Novel Protocol for Authentication and Group Key Distribution for Secure Transmission in VANET Environment Using Theory of Number 4th

A Novel Protocol for Authentication and Group Key Distributi...

引用

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

作者： Prakash, Ram Kumar, Vinod Gupta, Khushboo Pal, Om Kumar, Pravin Univ Allahabad Dept Elect & Commun Prayagraj India Univ Delhi Dept Comp Sci Delhi India

ISBN: (纸本)9783031837890;9783031837906

With significant technological advancements, Vehicular Ad-hoc Networks (VANETs), a form of Mobile Ad-Hoc Networks, have emerged as the most extensively utilized paradigm for intelligent transportation networks, with each vehicle outfitted with sensors serving as a mobile node. In order to guarantee traffic efficiency and safety, VANET systems are utilized to give real-time data about weather, traffic, and road conditions to vehicles. In order to provide exchange of messages in VANETs, the authenticity of vehicles becomes an important aspect that requires meticulous attention. In recent years, many authentication and group key distribution protocols have been presented, but they still suffer from performance efficiency and security vulnerabilities. In this work, we propose a multi-vehicle authentication protocol to provide safe communication in a VANET context. In our scheme, the trusted party server's computational cost for authentication, group key encryption, and decryption is significantly less than the related schemes. Additionally, we present a theory of number-based group key distribution protocol to distribute the group key for the verified vehicles. Our scheme reduces the rekeying cost to O(1) when a vehicle leaves or joins the group. The rigorous security analysis demonstrates that our proposed protocol ensures message authentication, forward and backward security, group key secrecy, and identity privacy while protecting the message against well-known security attacks. Performance analysis shows that our scheme significantly reduces the computation complexity, communication complexity, and storage complexity compared to existing works.

关键词： Authentication group key forward and backward security Theory of number

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A Comprehensive Analysis of Process Energy Consumption on Multi-socket Systems with GPUs 11th

A Comprehensive Analysis of Process Energy Consumption on Mu...

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11th Latin American Conference on High Performance Computing

作者： Leon-Vega, Luis G. Tosato, Niccolo Cozzini, Stefano Univ Trieste Trieste Italy AREA Sci Pk Trieste Italy

ISBN: (纸本)9783031800832;9783031800849

Robustly estimating energy consumption in High-Performance Computing (HPC) is essential for assessing the energy footprint of modern workloads, particularly in fields such as Artificial Intelligence (AI) research, development, and deployment. The extensive use of supercomputers for AI training has heightened concerns about energy consumption and carbon emissions. Existing energy estimation tools often assume exclusive use of computing nodes, a premise that becomes problematic with the advent of supercomputers integrating microservices, as seen in initiatives like Acceleration as a Service (XaaS) and cloud computing. This work investigates the impact of executed instructions on overall power consumption, providing insights into the comprehensive behaviour of HPC systems. We introduce two novel mathematical models to estimate a process's energy consumption based on the total node energy, process usage, and a normalised vector of the probability distribution of instruction types for CPU and GPU processes. Our approach enables energy accounting for specific processes without the need for isolation. Our models demonstrate high accuracy, predicting CPU power consumption with a mere 1.9% error. For GPU predictions, the models achieve a central relative error of 9.7%, showing a clear tendency to fit the test data accurately. These results pave the way for new tools to measure and account for energy consumption in shared supercomputing environments.

关键词： Energy measurement Supercomputers Microprocessors Multicore processing Load modeling High performance computing Cluster computing

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Discovering Visitor Profiles at Traditional Events on Boosting Domestic Tourism: A Multivariate Analysis 4th

Discovering Visitor Profiles at Traditional Events on Boosti...

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

作者： Machado, Renata Goncalves Santos, Deborah Oliveira Fernandes, Paula Odete Inst Politecn Braganca UNIAG Braganca Portugal Ctr Fed Educ Tecnol Minas Gerais Belo Horizonte MG Brazil

ISBN: (纸本)9783031840777;9783031840784

The Feira das Cantarinhas and Feira do Artesanato in Braganca is an event with social and economic importance for the city and its population, as well as for local tourism. Despite its relevance, there are no studies that analyse the impact of this event on tourism in the city and the event marketing strategies used. That way, this study aims to analyse the profile of the Feira das Cantarinhas and Feira do Artesanato visitors in 2023, identifying distinct groups based on demographic and professional characteristics. A survey was conducted by a questionnaire during the fair days in the 2023 edition. To respond to the objective of the study, a cluster analysis was carried out, and presented in this research. In the cluster analysis, were found three different groups, named Student Visitors, Occupational Diversity Visitors, and Third-Party Employed Visitors. The three Clusters identified have participants who are mostly residents of the municipality of Braganca, are between 18 and 25 years old, and are mostly female. The majority of visitors from the three groups went to the event accompanied. Regarding the differences that contrast the groups, they are mainly related to educational qualifications, professional situation, and the group accompanying the event.

关键词： Traditional events Domestic tourism Cluster Analysis Multivariate analysis

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Assessing the Impact of Decent Work on the Mental Health of Female Carers in Mauritius Through the Psychology of Working Theory 4th

Assessing the Impact of Decent Work on the Mental Health of ...

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4th International Conference on Society 5.0

作者： Kodye-Domah, Dayalutchmee Sobha, Leena Devi Lukea-Bhiwajee, Soolakshna Desai Univ Technol Sch Sustainable Dev & Tourism Port Louis Mauritius

ISBN: (纸本)9783031714115;9783031714122

Using the Psychology of Working Theory as a pillar, this study has aimed to examine the impact of decent work (subscales: interpersonal and physical safe working conditions, adequate health care, adequate compensation, hours that allow for free time and rest, and lastly, organisational values) directly and indirectly through the use of mediators namely, survival needs, social contribution needs, and self-determination needs. Moreover, the study also assessed the influence of the subscales of decent work on the mental health of female carers in Mauritius. Quantitative data was collected from 136 working women working in care homes or acting as carers for individual households through a questionnaire adopted from previous studies. The data analysis was done by use of SPSS 26.0. The findings showed that there were a positive and significant relationship between physical and interpersonal safe working conditions, access to adequate health care, and organisational and family values and mental health of female carers. In addition, the result could imply that there was a positive relationship related to survival needs, satisfaction needs and self-determination needs among individual carers and their mental health. The influence of the subscales of decent work on mental health showed a strong positive correlation but two factors namely, adequate compensation and hours that allow for free time and rest had low correlations. Overall, the findings suggested that decent work indeed contributed positively to the mental health of the female carers. Decent work promotes good psychological well-being of female workers because work is pivotal in the attainment of individual needs such as survival, social contribution, and self-determination. Implications for practice and future research are discussed.

关键词： Decent Work Mental Health Psychology of Working Theory

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Industry 4.0 Technologies in Distribution Centers: A Survey 5th

Industry 4.0 Technologies in Distribution Centers: A Survey

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5th 5th Olympus - International Conference on Supply Chain

作者： Daios, Adamos Kostavelis, Ioannis Int Hellen Univ Dept Supply Chain Management Kanellopoulou 2 Katerini 60132 Greece

ISBN: (纸本)9783031693434;9783031693441

Industry 4.0 technologies are gaining traction with every aspect of modern manufacturing and distribution procedures. In the context of supply chain management, where the flow of products and information is of the utmost importance, these technologies play a crucial role in achieving accuracy, speed, and flexibility while being cost effective. A central node of each supply chain is the distribution center. The main functionalities of distribution centers concern receiving, storing, packaging and dispatching goods, while managing all necessary information related to them. The paper at hand provides a short yet representative literature survey for the Industry 4.0 technologies that have been adopted so far in the logistics applications of contemporary distribution centers. The standard processes are outlined and examined in terms of their automation with novel applied or theoretical technological solutions. In addition, the emerging processes of distribution centers are discussed including demand forecasting and automation in last-mile delivery. Discussions and future trends are also presented, and the adoption pace of the new technologies is analyzed.

关键词： industry 4.0 distribution center robotics IoT cloud computing warehouse automation

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Enhancing Course Performance Reports: Leveraging ChatGPT for Comprehensive Data Analysis 5th

Enhancing Course Performance Reports: Leveraging ChatGPT for...

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5th International Symposium on Intelligent Computing Systems

作者： Bilquise, Ghazala Siddartha, Sharmila AlHassan, Munther Sunil, Bindu Al Ibaisi, Lama Higher Coll Technol POB 15725 Dubai U Arab Emirates

ISBN: (纸本)9783031829307;9783031829314

Course performance analysis is a critical process that involves the systematic review and evaluation of students' academic progress, course feedback, and learning outcome attainment at an institutional level. This analysis ensures that educational standards are met and identifies areas for improvement in teaching and curriculum. However, data analysis tends to be a time-consuming task which is susceptible to human bias and mistakes. The process is often hampered by challenges such as a lack of automation and decision support, making reviews time-consuming and prone to errors. To address these limitations, this study utilizes the capabilities of ChatGPT to analyze end-of-semester course assessment reports for three courses across seventeen campuses within a higher education institution based in the UAE. The study adopts a structured approach to analyze course performance data, highlighting the potential of ChatGPT as a tool for analyzing course performance, encompassing both qualitative and quantitative data. By leveraging ChatGPT, the study aims to streamline the course performance analysis process, making it more efficient. The insights provided by this study could be valuable for educational institutions looking to enhance their course evaluation methods and overall academic standards.

关键词： ChatGPT Course Performance Higher Education Qualitative analysis Quantitative analysis

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Accelerating Tomographic Artifact Removal Using a Multi-GPU System 11th

Accelerating Tomographic Artifact Removal Using a Multi-GPU ...

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11th Latin American Conference on High Performance Computing

作者： Macul, Larissa Miqueles, Eduardo Brazilian Synchrotron Light Lab CNPEM Campinas SP Brazil

ISBN: (纸本)9783031800832;9783031800849

Synchrotron-based x-ray micro-tomography systems often suffer from severe ring artifacts in reconstructed images. This article presents an implementation of superior techniques for eliminating these artifacts using cuda and high-performance computing (hpc) on a multigpu system. The proposed methods are designed to handle various types of stripe artifacts, providing efficient and high-quality results. Performance evaluations demonstrate significant speedup and enhanced artifact removal quality compared to traditional cpu based methods. Current implementations of this method in open packages do not offer acceleration in multi-gpu systems. In this sense, the present work makes a significant contribution to the imaging community by providing an implementation strategy in python/c/cuda, allowing acceleration.

关键词： ring artifacts tomography cuda hpc multi-gpu imaging reconstruction

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Integrating a Hybrid Lightweight Consensus Algorithm in HyperLedger Fabric 1st

Integrating a Hybrid Lightweight Consensus Algorithm in Hype...

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1st Global Internet of Things and Edge Computing Summit

作者： Michalopoulos, Fotis Vavilis, Sokratis Niavis, Harris Loupos, Konstantinos Inlecom Innovat Athens Greece

ISBN: (纸本)9783031785719;9783031785726

In today's interconnected IoT ecosystems, blockchain technology acts as a facilitator for decentralization. Hyperledger Fabric (HLF) is a renounced permissioned blockchain platform designed for enterprise use. Currently, HLF supports two primary consensus algorithms: Raft and smartBFT. Despite HLF's modular nature, integrating new consensus algorithms remains a challenging and intricate process that lacks sufficient guidelines. In this work, we bridge this gap by providing a novel, comprehensive, yet practical guide to simplify the integration of consensus algorithms into HLF. Furthermore, we demonstrate our approach by integrating a new hybrid algorithm into HLF, specifically tailored for the IoT ecosystem. Compared to the current algorithms, experiments indicate promising performance and reliability.

关键词： Blockchain IoT Hyperledger Fabric Consensus Algorithm Implementation Guidelines

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