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检索条件"任意字段=21st International Conference on Computer Systems and Technologies, CompSysTech 2020"

共 1297 条记录，以下是41-50 订阅

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Integrating machine learning and IoT into apiary management to optimize bee health and production 19

Integrating machine learning and IoT into apiary management ...

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19th international conference on Future Networks and Communications, FNC 2024 / 21st international conference on Mobile systems and Pervasive Computing, MobiSPC 2024 / 14th international conference on Sustainable Energy Information Technology, SEIT 2024

作者： Vatskel, Vladimir Biloshchytskyi, Andrii Neftissov, Alexandr Kuchanskyi, Oleksandr Andrashko, Yurii Biloshchytska, Svitlana Sachenko, Illia Astana IT University Astana010000 Kazakhstan Department of Information Technologies Kyiv National University of Construction and Architecture Kiev01001 Ukraine Department of Information Systems and Technology Taras Shevchenko National University of Kyiv Kyiv01001 Ukraine Department of System Analysis and Optimization Theory Uzhhorod National University Uzhhorod88000 Ukraine

This article discusses integrating machine learning and IoT technologies into bee apiary management systems. The research aims to optimize bee conditions, improve their health and increase honey production. Using machine learning, large amounts of data collected by hive sensors are analyzed to predict and prevent various bee health problems effectively. Particular attention is paid to methods of controlling temperature in hives through pulse-width modulation technology, which provides accurate and energy-efficient regulation of temperature conditions. A method for predicting temperature in hives for effective apiary management is also proposed. © 2024 The Authors. Published by Elsevier B.V.

关键词： beekeeping energy efficiency Internet of Things machine learning pulse width modulation sustainable agriculture temperature control

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Automated Quality Control of 3D Printed Tensile Specimen via computer Vision 21st

Automated Quality Control of 3D Printed Tensile Specimen via...

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21st international conference on Smart technologies and Education (stE) on Smart technologies for a Sustainable Future

作者： Ullah, Rizwan Gebrehiwot, Silas Patabendige, Thumula Madduma Espinosa-Leal, Leonardo Arcada Univ Appl Sci Sch Engn Culture & Wellbeing Jan Magnus Janssons Plats 1 Helsinki 00560 Finland Arcada Univ Appl Sci Grad Sch & Res Jan Magnus Janssons Plats 1 Helsinki 00560 Finland

ISBN: (纸本)9783031618901;9783031618918

This research explores the integration of a robotic arm using computer vision for automated quality control for sorting 3D printed tensile specimens. The study, conducted, focuses on utilizing a Niryo NED-2 robotic arm with a vision system. The robotic arm captures cross-sections of tensile specimen, and a Python program processes vision feeds, filtering images based on 2D contours. Tensile samples were manufactured using Fused Deposition Modeling (FDM) with PLA material, incorporating known offsets (both positive and negative). Their dimensions were predicted and compared with the actual geometrical measurements. Experimental results showcase the system's accuracy in measuring specimen dimensions, demonstrating low error rates. The study highlights the potential for automated quality control in additive manufacturing, presenting a valuable tool for Industry 4.0. The robotic arm's vision system proves effective in enhancing efficiency and reliability in 3D printing quality inspection processes.

关键词： Machine vision Industry 4.0 Additive manufacturing Robots

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An Intelligent Chatbot Supporting students in Massive Open Online Courses 21st

An Intelligent Chatbot Supporting Students in Massive Open O...

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21st international conference on Web-Based Learning / 7th international Symposium on Emerging technologies for Education (ICWL/SETE)

作者： Calabrese, Alessio Rivoli, Alessio Sciarrone, Filippo Temperini, Marco Univ Mercatorum Fac Econ Piazza Mattei 10 Rome Italy Sapienza Univ Rome DIAG Dept Comp Control & Management Engn Rome Italy

ISBN: (纸本)9783031330223;9783031330230

In recent years, there has been an exponential growth in the demand for distance learning. In addition, the pandemic from COVID-19 has radically changed the techniques but also the teaching/learning methodologies. In such a picture, Massive Online Open Courses (MOOCs), are increasingly emerging on the Web. Due to the big number of students, in MOOCs the traditional monitoring and instructional interventions, performed by the teacher on individual learners, are of difficult, if not impossible, application. An Artificial Intelligence approach, based on Virtual Conversational Agents or Intelligent Chatbots, can help overcoming such difficulties. In this paper we present a system, at its early stage of development and based mainly on a deep learning model, able, at different levels, to suggest didactic material to students in a query/answer modality. It is able to answer students' questions by proposing didactic material taken both from a specific knowledge domain or from Wikipedia. We investigate the potential of such an early stage implementation, through several case studies. We also present a qualitative evaluation, based on the case studies findings, which we think is encouraging towards the development and field experimentation of a whole system.

关键词： MOOCs Intelligent Chatbot Deep Learning Help systems

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Comparative Analysis of Machine Learning Techniques for DDoS Intrusion Detection in IoT Environments 21

Comparative Analysis of Machine Learning Techniques for DDoS...

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21st international conference on Smart Business technologies, ICSBT 2024

作者： Chukwukelu, Godwin Essien, Aniekan Salami, Adewale Imram Utuk, Esther IT Services Department Forctix Ltd. London United Kingdom Operations and Management Science Healthcare & Innovation Group University of Bristol Bristol United Kingdom Department of Computer Science Leadcity University Ibadan Nigeria University of East Anglia Norwich United Kingdom

ISBN: (纸本)9789897587108

This study addresses the challenge of Distributed Denial of Service (DDoS) attacks in the Internet of Things (IoT) environment by evaluating the effectiveness of Intrusion Detection systems (IDS) using machine learning techniques. Due to the lightweight computational configuration of IoT systems, there is a need for a classifier that can efficiently distinguish between legitimate and malicious network traffic without demanding substantial computational resources. This research presents a comparative analysis of four machine learning models: (i) k-Nearest Neighbour (k-NN), (ii) Support Vector Machine (SVM), (iii) Random Forest (RF), and (iv) Multilayer Perceptron (MLP), to propose a lightweight DDoS intrusion detection classifier. A novel classification model based on the MLP architecture is proposed, focusing on minimalistic design and feature reduction to achieve accurate and efficient classification. The model is tested using the CICIDS2017 dataset and demonstrates high accuracy and computational efficiency, making it a viable solution for IoT environments where computational resources are limited. The findings show that the proposed µML-IDS model achieves an accuracy of 99.8%, F-score of 96.5%, and precision of 99.96%, with minimal computational overhead, highlighting its potential for real-world application in protecting IoT networks against DDoS attacks. Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Network intrusion

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Architecture of intercomponent interaction of a microprocessor-powered sustainable relay protection system based on open standards IoT 19

Architecture of intercomponent interaction of a microprocess...

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作者： Neftissov, Alexandr Biloshchytskyi, Andrii Kirichenko, Lalita Kazambayev, Ilyas Zhakupova, Kamila Urazayev, Dnislam Vatskel, Vladimir Astana IT University Astana010000 Kazakhstan Department of Information Technologies Kyiv National University of Construction and Architecture Kyiv01001 Ukraine

Nowadays, the problem of the coordination of relay protection systems during faults becomes widespread, as the trip of the circuit breaker must be fast. One of the solutions is the application of the Internet of Things. The object of this research is a relay protection system architecture, which uses elements of the Internet of Things and is based on open standards. The research aims to address the efficiency of using the Internet of Things in the relay protection systems, as well as the relevance of the proposed architecture. This architecture unifies processes encompassing relay protection, data collection, analysis, real-time communication protocols, and secure data transmission techniques. Apart from the increased quality of relay protection, it provides a well-organized framework for the precise and efficient acquisition, transmission, and analysis of data. As a result of the research, it was derived that the Internet of Things can be used in relay protection applications along with the proposed architecture if certain conditions are met. © 2024 The Authors. Published by Elsevier B.V.

关键词： Relay control systems

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Using Escape Rooms to Support Learners' 'Equations-Escapes' in e-learning: Applied MCIEC Model in Mathematics Learning

Using Escape Rooms to Support Learners' 'Equations-Escapes' ...

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2024 international conference on Applied Mathematics and computer Science, ICAMCS 2024

作者： Paraskeva, F. Aggelopoulou, I. Fragos, C. Bouta, H. University of Piraeus Department of Digital Systems Karaoli Dimitriou 80 Piraeus Greece

ISBN: (纸本)9798350363807

In the contemporary digital age, particularly in the wake of the COVID-19 pandemic, educators are faced with the need to adapt and integrate new pedagogical approaches and technologies into the realm of education. Within the domain of mathematics, longstanding challenges in comprehending the subject have been widely recognized. In response to this issue, an electronic course entitled 'Equations-Escapes from the Museum of Mathematics' has been developed to enhance sixth-grade mathematics instruction. This course employs digital escape rooms and is delivered entirely online through the Moodle platform, following the MCIEC model. Its primary objective is to augment mathematical proficiency, cultivate student motivation, and nurture 21st-century competencies. The efficacy of the course was appraised through a pilot study involving 50 educators, and subsequent statistical analysis of the collected data substantiated the validation of all alternative hypotheses devised in accordance with the research inquiries. Consequently, this educational intervention is deemed successful in achieving the stated objectives. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhanced Intrusion Detection in Wireless Sensor Networks: A Voting and Particle Swarm Optimization Approach 21

Enhanced Intrusion Detection in Wireless Sensor Networks: A ...

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21st IEEE international conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Jaradat, Ameera Alhatamleh, Salem Nasayreh, Ahmad Gharaibeh, Hasan Yarmouk University Dept. of Computer Sciences Irbid Jordan

ISBN: (纸本)9798350378078

Wireless sensor networks (WSNs) are of great importance in advanced technologies in the modern developing world as they are widely used in many applications. Although encryption and authentication protocols in WSNs are widely adopted, there are continuous expectations of new sophisticated attack strategies that are spreading, which pose high risks and threats. This work addresses these challenges using Intrusion Detection systems (IDS) that will be supported by machine learning algorithms. Specifically, it explores the WSN-DS dataset that includes network activities and attack types. In addition, it initializes the importance of feature selection and proposes data balancing methods such as Synthetic Minority Over-sampling Technique SMOTE, it is a statistical method for correcting class imbalance in datasets by creating synthetic samples for the minority class to enhance the detection rate. To increase the performance of the machine learning model, we use a voting classifier consisting of Random Forest (RF), K-Nearest Neighbors (KNN) and Extreme Gradient Boosting (XGBoost) and optimize them using Particle Swarm Optimization (PSO) by selecting the best hyperparameters. This ensemble method aims to maximize the performance of the used ensemble models. The proposed model is demonstrated by improving the PSO voting classifier model which gives a remarkable accuracy of 99.99% making it a highly effective approach in predicting and filtering different types of attacks in wireless sensor networks. This work will help in promoting further research in protecting wireless sensor networks from invasive intrusions in various applications to ensure security in communication networks. © 2024 IEEE.

关键词： Network intrusion

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Digital Twins of Smart Campus: Performance Evaluation Using Machine Learning Analysis 21

Digital Twins of Smart Campus: Performance Evaluation Using ...

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21st IEEE/ACIS international conference on Software Engineering Research, Management and Applications, SERA 2023

作者： Hussaini, Adamu Qian, Cheng Guo, Yifan Lu, Chao Yu, Wei Towson University Dept. of Computer & Information Sciences United States

ISBN: (纸本)9798350345889

The Internet of Things (IoT) paradigm is gradually becoming more prevalent through numerous devices and technologies, including sensors, actuators, microcontrollers, cloud-enabled services, and analytics. IoT objects gain intelligence by integrating with wireless sensor networks (WSNs), mobile computing and communication, and others. With sensors, smart things can be enabled by monitoring and identifying environmental changes related to motion, temperature, humidity, pressure, light, vibration, etc. To timely keep track of state changes, researchers are considering developing a cyber replicator, denoted as Digital Twin (DT), of real physical systems as a way to visualize, model, and work with complex cyber-physical systems (CPS). In this paper, we first refine the dataset to a format that can be easily used for deep learning (DL) experiments, IoT data pipeline development, data modeling and simulation, data aggregation, etc. We then demonstrate that DT data can be used to determine space occupancy based on the ambient light sensor, which tends to indicate occupancy in particular spaces because the building has smart lighting that will switch off when rooms are unoccupied after a certain time. Given the apparent developments in machine learning technology, it is clear that machine learning-based prediction has the ability to enhance resource utilization and further forecast future events. Particularly, we use a DT-based dataset and Long-Short-Term Memory (LstM) neural network architecture to forecast the campus building's internal temperature. © 2023 IEEE.

关键词： Internet of things

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Personalized Federated Learning with Kolmogorov-Arnold Enhanced CNN for Rotating Equipment Fault Diagnosis 21

Personalized Federated Learning with Kolmogorov-Arnold Enhan...

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21st international computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Kulevome, Delanyo Kwame Bensah Man, Qiu Mawuli, Ernest Smith Opoku-Mensah, Edward Qilu Institute of Technology Department of Computer and Information Engineering Shandong Jinan250200 China Qilu Institute of Technology Department of Intelligent Manufacturing and Control Engineering Shandong Jinan250200 China Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development Department of Electrical/Electronic Technology Education Kumasi Ghana HEC Department of Information Technologies Montréal Canada

ISBN: (纸本)9798331519254

Fault diagnosis of rotating equipment plays a crucial role in ensuring operational reliability and minimizing downtime in industrial systems. This study proposes a novel approach that integrates personalized federated learning (PFL) with a Kolmogorov-Arnold network (KAN)-enhanced convolutional neural network (CNN) to improve diagnostic accuracy across heterogeneous operational environments. The framework is designed to address variations in equipment conditions and data distributions, providing personalized models tailored to specific clients. A comprehensive evaluation is conducted using benchmark datasets, demonstrating significant improvements in fault classification accuracy, model interpretability, and training efficiency under non-IID data settings using the KAN-enhanced CNN model. This study contributes to advancing predictive maintenance practices, paving the way for resilient and intelligent industrial systems. © 2024 IEEE.

关键词： Federated learning

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Two-stage Enhancement for Recommendation systems Based on Contrastive Learning 21st

Two-Stage Enhancement for Recommendation Systems Based on Co...

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21st international conference on Web Information systems and Applications (WISA)

作者： Sun, Siyu Cai, Tianyu Yan, Fanli Ju, Shenggen Sichuan Univ Chengdu 610065 Sichuan Peoples R China

ISBN: (纸本)9789819777068;9789819777075

Knowledge-aware recommendation systems use knowledge graphs as side information to enhance the performance of recommendation systems. The existing methods often employ graph neural networks to process the relational networks and use contrastive learning to obtain more effective node representations. However, persistent challenges from active users' noisy data and the cold-start problem related to inactive users impact model performance. Recent studies mainly use data augmentation methods to address these issues but only consider the enhancement during the training stage and neglect the pre-training stage. For this reason, we propose a Two-stage Enhancement for Recommendation systems based on Contrastive Learning (TERSCL). This model primarily integrates two stages of data augmentation methods: one is the pre-training enhancement, where an enhanced adjacency matrix is obtained for subsequent training. The other generates a distinct dropout subgraph during each training round to participate in the subsequent graph encoding and contrastive learning process. Extensive experiments conducted on three benchmark datasets verify that TERSCL can significantly improve recommendation performance and mitigate the cold-start problem.

关键词： Recommendation system Graph neural network Knowledge graph Contrastive learning Data augmentation

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