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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Intrusion Detection System for IoT Security 3rd

Intrusion Detection System for IoT Security

引用

3rd International Conference on Network Security and Blockchain Technology

作者： Das, Sudeshna Majumder, Abhishek Tripura Univ Comp Sci & Engn Dept Agartala India

ISBN: (纸本)9789819780501;9789819780518

Our everyday lives are getting increasingly connected with the Internet of Things, which presents serious worries about potential cybersecurity threats and the need for trustworthy solutions. Deep learning-based intrusion detection can increase the efficacy of the system. However, it is especially difficult to use these models in the Internet of Things. In this work, a lightweight intrusion detection model, using Feedforward Neural Network, has been developed. Model compression techniques, such as weight and neuron pruning, are applied to feedforward neural network. Performance evaluations of the baseline intrusion detection model and the proposed model have been conducted. Based on experimental results, the proposed model outperforms the baseline in terms of training time by an average of 93% while exhibiting very little degradation in performance.

关键词： Intrusion detection system Feedforward neural network Weight pruning Neuron pruning

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Optimizing Underwater Acoustic Survey Strategies with Autonomous Underwater Vehicles: A Simulation-Based Framework 21st

Optimizing Underwater Acoustic Survey Strategies with Autono...

引用

21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Lagudi, Antonio Severino, Umberto Bruno, Fabio Univ Calabria Dept Mech Energet & Management Engn I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9783031809453;9783031809460

Ocean exploration and inspection missions involving underwater robotic systems have gained increasing popularity in various domains such as marine science, archaeology, and defense in recent years. However, deploying vessels and robotic solutions for such research activities entails significant costs for acquisition and maintenance. This is primarily due to the logistical challenges involved in transporting equipment between locations, which necessitates a substantial financial investment, in addition to expenses associated with crew and research personnel. Effective planning of survey campaigns is crucial to avoid costly missteps arising from poor organization. It ensures that expenditures are justified and that objectives are achieved without unnecessary setbacks or errors. In this context, the utilization of simulation methodologies adds invaluable value in mitigating costs and exploring various solutions before the mission is carried out in the real world. Their utility extends beyond the examination of specific sensors or setups;they are also a valuable tool for planning different campaigns with different mission objectives. In this paper, a simulation framework that integrates an Autonomous Underwater Vehicle with a Multibeam Echo Sounder as the perception sensor is presented. The objective is to streamline acoustic survey mission planning and generate synthetic acoustic image data incorporating the modeling of typical error sources, simplifying the evaluation of output data for real applications. This system can also be utilized as a Software-in-the-loop system for testing and verification of control systems and algorithms.

关键词： Simulation AUV Underwater Robotics Acoustic Imaging Unity3D Robot Operating System

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High-Payload RDH Technique for Secure Data Transmission Through Improved Context Pixel-Based PVO Exploiting Center-Folding Strategy 3rd

High-Payload RDH Technique for Secure Data Transmission Thro...

引用

3rd International Conference on Network Security and Blockchain Technology

作者： Meikap, Sudipta Jana, Biswapati Hijli Coll Dept Comp Sci Paschim Medinipur 721306 W Bengal India Vidyasagar Univ Dept Comp Sci Midnapore 721102 W Bengal India

ISBN: (纸本)9789819780501;9789819780518

In this study, a center folding method and context pixel-based PVO (CPPVO) are used to provide a large capacity reversible data hiding technique for safe data transfer. Dual Marked Images (DMI) were initially created by inserting concealed information into an interpolated cover image using the center folding approach. More hidden information on Dual Marked Images is then included using context pixel-based PVO. In this instance, the center folding strategy was chosen because to its ability to compress significant hidden information using averages. CPPVO, on the other hand, provides the capability to embed an image block with over two pixels by applying context and neighboring pixels. The proposed method is highly flexible due to the fluctuating amount of the secret data and the size of various picture block needed for efficient embedding process. In the experiment, findings show that the proposed method Improved Context-Pixel-Based PVO (ICPPVO) that uses the center folding approach is especially suitable for embedding more hidden information without giving in the picture quality in comparison to earlier state-ofthe-art approaches. The anticipated result revealed a number of exceptional skills in the areas of digital forgery detection, tamper detection, and covert data transfer, all of which are crucial to the advancement of technology. A number of public and private sector areas, including defense, health care, business security, as well as intellectual property rights, would gain tremendously from this initiative.

关键词： Reversible data hiding Context pixel-based PVO Center-Folding Payload PSNR Steganography

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Electronic Identification of Health Professionals in Digital Health Service Provision-A Case Study in Brazil 9th

Electronic Identification of Health Professionals in Digital...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Karunaratne, Thashmee Brunet, Joao Assom, Luigi Stockholm Univ Dept Comp & Syst Sci Stockholm Sweden Fed Univ Campina Grande UFCG Campina Grande Brazil

ISBN: (纸本)9789819754403;9789819754410

Electronic identification and authentication, in contrast to classical password-based authentication systems, is reaching a level of the norm of person identification in a digital-by-default society. Secure, trusted as well as simple, lightweight, easy to use and privacy-preserved solutions are hence in demand, specifically when government services are offered through digital means. Digital health or eHealth services, however, are relatively complex due to the additional privacy and security needs in managing patient data. The European eIDAS regulation is enabling digital public services authentication, predominantly targeting EU citizens. This paper highlights advantages of an eID wallet in mitigating challenges in the Brazilian healthcare system and aligning to European standards. A Sweden-Brazil healthcare collaboration is used as a case study of electronic identification of health professionals for provision at care units in Brazil. The outcome of the paper includes the potential advantages of an eID wallet to mitigate some of the identified challenges in the Brazilian healthcare system. It also extends the analysis to a comparison of such eID wallet requirements for use in the European context. Based on the consolidated requirement list derived in this paper a technical system for an eID wallet that complies with both European and Brazilian regulations can be designed.

关键词： Digital healthcare eID wallet eIDAS regulation ICP Brazil

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Active Learning for Image Classification: A Comprehensive Analysis in Agriculture 9th

Active Learning for Image Classification: A Comprehensive An...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Flores, Christopher A. Valenzuela, Ariel I. Verschae, Rodrigo Univ OHiggins Inst Engn Sci Rancagua 2841959 Ohiggins Chile

ISBN: (纸本)9789819754403;9789819754410

Precision agriculture allows for the sustainable improvement of agricultural products by introducing technologies that provide crop-specific data, which supervised algorithms can process. However, supervised algorithms require expert-labeled data, which can be highly expensive in agricultural applications. Given this problem, active learning (AL) arises as an alternative to reduce the need to annotate training examples manually. This paper analyzes the use of AL in classifying agricultural crop images. To evaluate AL, two datasets with information on fruit and vegetable images were used on two neural network-based algorithms. The classification results indicate that AL reduced the number of training examples to achieve a given performance. Additionally, the pseudo-labels of the supervised algorithms, a stopping criterion, and the explainability of the predictions were analyzed. These analyses allowed to assess the applicability of AL in agriculture to understand the learning process of the supervised algorithms.

关键词： Active learning Agriculture Convolutional neural networks Explainable artificial intelligence

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Bird Detection and Recognition systems Through Machine Learning and Artificial Intelligent: Advancements and Future Opportunities 21st

Bird Detection and Recognition Systems Through Machine Learn...

引用

21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Rossi Labianca, Lautaro Alvarez Uruena, Jaime Curto Hernandez, Javier Garcia Serrada, Raul Air Inst IoT Digital Innovat Hub Spain Deep Tech Lab Ave SantiagoMadrigal 39 Salamanca 37003 Spain Univ Salamanca BISITE Res Grp Edificio Multiusos IDICalle Espejo 2 Salamanca 37007 Spain

ISBN: (纸本)9783031820724;9783031820731

The paper provides an overview of advancements in bird detection and recognition systems using Machine Learning and Artificial Intelligence (AI). It highlights the increasing adoption of wind farms amid rising electricity demand, underscoring their environmental impact on avian species. To address these ecological challenges, the development of bird recognition solutions is crucial. The paper analyzes various techniques, including radar systems, sound recognition, Convolutional Neural networks (CNNs), electromagnetic detection, YOLOv5, and color segmentation, discussing their features, computational costs, and constraints. It concludes that while deep learning models offer superior results, they need a balance between accuracy and speed, alongside training with large and representative datasets. Ultimately, the paper aims to contribute to efforts aimed at mitigating the adverse effects of wind farms on bird populations through advanced technologies. Additionally, through this paper we intend to shed light over the state of the art on bird detection systems and provide insights that intends to solve some of these drawbacks.

关键词： Bird Recognition Bird detection Machine Learning Artificial Intelligence Computer Vision

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Leveraging Machine Learning and Deep Learning Models for Enhanced Stock Price Prediction: A State-of-the-Art Analysis 21st

Leveraging Machine Learning and Deep Learning Models for Enh...

引用

21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Alaoui, Safae Belamfedel Hafid, Abdelatif Sayyouri, Mhamed Rahouti, Mohamed Ecole Natl Sci Appl ENSA LISA 72 Fes Morocco Ecole Super Ingn Sci Appl ESIS ESISA Analyt 505 Fes Morocco Fordham Univ Comp & Informat Sci Dept New York NY USA

ISBN: (纸本)9783031820724;9783031820731

The prediction of stock prices has recently gained considerable attention as a complex and challenging issue within the realms of economics and finance. Stock prices are affected by various factors, such as the business environment, stock market operations, inflation, and unexpected events. Since the stock market is volatile and nonlinear, finding the most effective model to forecast stock prices is one of the most challenging problems. Researchers have increasingly explored various Machine Learning (ML) and Deep Learning (DL) models to address this issue due to their capacity to handle time series data and nonlinear patterns. These models often outperform traditional approaches in predicting stock prices with high accuracy and lower root mean square error (RMSE). This paper reviews various works that have utilized ML approaches for stock price prediction, covering research published between 2017 and 2023. This literature review discusses various techniques, their performance, limitations, and future work. We assess the latest techniques in many studies, including ML and DL models. The findings of this review conclude that Neural networks (NNs) are the most commonly used approaches in predicting stock prices due to their effectiveness in detecting complex patterns in financial data.

关键词： Stock price prediction Machine Learning Deep Learning Neural networks Sentiment Analysis

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A NIMFA Epidemiological Model for Analyzing Malware Behavior in IoT networks 9th

A NIMFA Epidemiological Model for Analyzing Malware Behavior...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Galvan, Martin Montoya, German A. Lozano-Garzon, Carlos Univ Andes Bogota Colombia

ISBN: (纸本)9789819754403;9789819754410

The Internet of Things describes the network of electronic devices that are consistently interconnected via Internet. These devices, which encompass sensors, software, and similar mediums, interact with other devices or systems. As the quantity of these interconnected devices on the Internet has surged dramatically, there has been an increasing interest among certain malicious individuals to exploit these devices for their own gain. Anotable instance of this is illustrated by the Mirai Botnet case. To counteract the potential manipulation of these devices by malicious entities, researchers have embarked on proposing models to comprehend the behaviors of these Botnets and their propagation patterns. In this sense, we aim to investigate one of these models, namely the NIMFA model. This model is noteworthy for its impressive scalability, enabling effective modeling of networks with up to 100,000 nodes with quadratic time complexity. Consequently, we propose to design a scalable SIS stochastic epidemiological model based on the N-intertwined Mean-Field Approximation (NIMFA) model for security analysis in IoT networks. Moreover, the outcomes of the NIMFA model closely align with those of other models, such as the Gillespie Simulation Algorithm.

关键词： Iot networks Malware behavior Epidemiological model NIMFA model Markov model

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Designing User Experience in the Context of Human-Centered AI and Generative Artificial Intelligence: A Systematic Review 21st

Designing User Experience in the Context of Human-Centered A...

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21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Alberto Pelaez, Carlos Solano, Andres Nunez, Juan M., V Castro, David Cardona, Juan J. Duque, Juan S. Espinosa, Juan C. Montano, Ana S. De la Prieta, Fernando Univ Autonoma Occidente Cali 760030 Valle Del Cauca Colombia Univ Salamanca BISITE Res Grp Salamanca 37007 Spain Corp Talentum Cali Valle Del Cauca Colombia

ISBN: (纸本)9783031820724;9783031820731

This study presents an assessment of the state-of-the-art based on a systematic literature review methodology, focusing on the problematic relationship between User Experience (UX) design, generative artificial intelligence (GenAI), and human-centered design. It examines how UX principles and generative AI technologies can be harmoniously integrated to enhance user experiences, prioritizing human needs and values in the design process. The findings reveal significant insights into ethical considerations, user personalization, the importance of human-centric approaches, trust and perception challenges, and advances in human-AI interaction.

关键词： HCAI User experience (UX) GenAI

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Enhancing Recruitment with LLMs and Chatbots 21st

Enhancing Recruitment with LLMs and Chatbots

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21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Novais, Liliana Rocio, Vitor Morais, Jorge Univ Aberta Lisbon Portugal Inst Super Engn Porto Porto Portugal SEG Automot Portugal Vila Do Conde Portugal INESC TEC Porto Portugal

ISBN: (纸本)9783031809453;9783031809460

Traditional approaches in the competitive recruitment landscape frequently encounter difficulties in effectively identifying exceptional applicants, resulting in delays, increased expenses, and biases. This study proposes the utilisation of contemporary technologies such as Large Language Models (LLMs) and chatbots to automate the process of resume screening, thereby diminishing prejudices and enhancing communication between recruiters and candidates. Algorithms based on LLM can greatly transform the process of screening by improving both its speed and accuracy. By integrating chatbots, it becomes possible to have personalised interactions with candidates and streamline the process of scheduling interviews. This strategy accelerates the hiring process while maintaining principles of justice and ethics. Its objective is to improve algorithms and procedures to meet changing requirements and enhance the competitive advantage of talent acquisition within organisations.

关键词： Large Language Models Chatbots Automation Efficiency Bias Mitigation

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