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检索条件"任意字段=24th International Conference on Control Systems and Computer Science, CSCS 2023"

共 927 条记录，以下是201-210 订阅

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AI-Based Feature Extraction and Cosine Similarity for Automation of Student Learning Assessment 24

AI-Based Feature Extraction and Cosine Similarity for Automa...

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24th international Arab conference on Information Technology, ACIT 2023

作者： Wijanarko, Bambang Dwi Bachtiar Hassan, Rosilah Binti Murad, Dina Fitria Ihsan, Ridho Bramulya Heryadi, Yaya Kuncoro, Engkos Achmad Julianti, Eka Binus Online Learning Bina Nusantara University Computer Science Department Jakarta Indonesia Graduate School Universitas Terbuka Master of English Education Jakarta Indonesia Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Binus Online Learning Bina Nusantara University Information Systems Department Jakarta Indonesia Binus Online Learning Bina Nusantara University Manajemen Department Jakarta Indonesia Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta Indonesia Universitas Terbuka Information System Department Jakarta Indonesia

ISBN: (纸本)9798350384307

this research is ongoing research into the student learning process which aims to develop artificial intelligence-based technology to calculate essay exam scores automatically, based on the textual proximity of students' answers to the answer key. this research uses an approach based on natural language processing (NLP) and text analysis. Feature extraction was carried out using the BERT model and similarity comparison using the Cosine distance method. Model evaluation was carried out using sensitivity and specificity tests. the model evaluation results show a sensitivity of 98.21%, which means this assessment model is able to detect positive cases well. © 2023 IEEE.

关键词： artificial intelligence BERT essay exam score natural language processing transformer

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Heartbeat Abnormality Detection in Phonocardiogram Signals using Wavelet Time Scattering and Optimized KNN Classification 24

Heartbeat Abnormality Detection in Phonocardiogram Signals u...

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24th international Arab conference on Information Technology, ACIT 2023

作者： Tiwari, Shamik Maheshwari, Piyush School of Computer Science University of Petroleum and Energy Studies Dehradun India Dubai United Arab Emirates

ISBN: (纸本)9798350384307

Heart auscultation continues to play an essential role in heart health diagnosis. However, many places worldwide have a shortage of suitably qualified medical practitioner's adept at this ability. this highlights the critical need to develop accurate automated systems for evaluating Phonocardiogram (PCG) data. PCGs are acoustic recordings that capture the noises made by the heart during its systolic and diastolic cycles. To solve this issue, we suggest using Wavelet Time Scattering with an optimized XGBoost classifier and K-Nearest Neighbors (KNN) classifier to detect irregular heartbeats in PCG signals. the results are promising, as the optimized KNN classifier obtains an impressive accuracy rate of 92.5% when combined with five-fold cross-validation, which is better than XGBoost classifier, which gains 87.93%. this demonstrates the efficacy of the optimized KNN in improving the automated interpretation of PCG data and assisting in the early diagnosis of heart-related problems. © 2023 IEEE.

关键词： PCG signals Wavelet scattering XGBoost, KNN

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An Autonomous Self-learning and Self-adversarial Training Neural Architecture for Intelligent and Resilient Cyber Security systems 1

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24th international conference on Engineering Applications of Neural Networks (EANN)

作者： Demertzis, Konstantinos Iliadis, Lazaros Hellen Open Univ Sch Sci & Technol Informat Studies Patras Greece Democritus Univ Thrace Sch Engn Fac Math Programming & Gen Courses Dept Civil Engn Xanthi Greece

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

Cybersecurity systems have become increasingly important as businesses and individuals rely more on technology. However, the increasing complexity of these systems and the evolving nature of cyber threats require innovative solutions to protect against cyber attacks. One promising approach is the idea of autonomous self-learning and auto-training neural architectures. Autonomous self-learning refers to the ability of the system to adapt to new threats and learn from past experiences without human intervention. Auto-training, on the other hand, refers to the ability of the system to improve its performance over time by automatically adjusting its parameters and algorithms. this research proposes an autonomous Self-Learning and Self-Adversarial Training (SLSAT) neural architecture for intelligent and resilient cyber security systems. It is an extension of the next-generation Continuous-Time Reservoir Computing (CTRC) that was proposed by the authors recently. the CTRC is a time-series anomaly detection system controlled by time-varying differential equations. It uses Reinforcement Learning (RL) to dynamically fine-tune the reservoir computing parameters in order to identify the aberrant changes in the data. the proposed method in this research improves the CTRC's architecture by including a Conditional Tabular Generative Adversarial Network (CTGAN). Specifically, including CTGAN allows the SLSAT architecture to generate synthetic data based on the identified abnormalities to improve the model's performance and adapt to new and evolving threats without manual intervention. this, as proved experimentally, helps the model identify aberrant changes in the data and fend off poison and zero-day attacks.

关键词： Reservoir Computing Continuous-Time Reservoir Computing Cyber Defense Time Series Analysis

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Using Intelligent Tutoring on the First Steps of Learning to Program: Affective and Learning Outcomes 24th

Using Intelligent Tutoring on the First Steps of Learning to...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Pankiewicz, Maciej Baker, Ryan Ocumpaugh, Jaclyn Warsaw Univ Life Sci Warsaw Poland Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9783031363351;9783031363368

there exist several online applications for automated testing of the computer programs that students write in computer science education. Use of such systems enables self-paced learning with automated feedback delivered by the application. However, due to the complexity of programming languages, even the easiest tasks made available through such systems require understanding of several programming concepts and formatting. therefore, a student's initial work in an introductory computer science course may be highly challenging, especially for students with no previous programming background. To address this challenge, a highly-decomposed micro-task module has been developed and made available on an automated assessment platform with programming assignments. Impact of its introduction has been examined within an introductory programming university course with 239 participants. We investigated the micro-task module's impact on student affect, student performance on the platform, and student learning outcomes. Results of the experiment show that students in the experimental group (with micro-tasks enabled) significantly less frequently reported frustration, confusion and boredom, needed less time to solve tasks on the platform and achieved significantly better results on the final test.

关键词： computer science education Programming micro-tasks Affect

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Reducing the energy consumption required for the disassembly process implemented on an assembly and disassembly line

Reducing the energy consumption required for the disassembly...

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international conference on control systems and computer science (cscs)

作者： Paun Marius-Adrian Dragomir Otilia Elena Dragomir Florin Iliescu Sergiu Stelian Minca Eugenia Duca Octavian Gabriel Gurgu Ion Valentin Department of Automation and Industrial Informatics University “Politehnica” of Bucharest Bucharest Romania Faculty of Electrical Engineering Electronics and Information Technology Valahia University of Targoviste Targoviste Romania Faculty of Automatic Control and Computers University “Politehnica ” of Bucharest Bucharest Romania Department of Automation and Electrical Engineering University “Dunarea de Jos ” of Galati Galati Romania Institute of Multidisciplinary Research for Science and Technology Valahia University of Targoviste Targoviste Romania

this paper proposes a method to reduce the energy consumption of an industrial disassembly process implemented on a production line by using an energy-efficient secondary disassembly process. In the case of defective products, the flow directs the simple product with priority to be disassembled in workstation (WS) 7. therefore, all defective products are redirected to WS7. the disassembly of all simple products in WS7 is problematic for two reasons: the power consumption of WS7 is high compared to the other workstations, additionally there is an increased possibility of bottlenecks in the production line due to the high disassembly time. the proposed solution to reduce the possibility of deadlock and reduce power consumption is to redirect the defective products to the flexible cell (FC). Handling robots located in neighboring stations will participate collaboratively in the disassembly process. the control structure ensures that the two robots "meet" in the boundary plane of WS3 with WS4 and disassemble each component through collaborative tasks. Collaborative control of disassembly is thought of as a solution to the global "fluidization" of production, removing the bottleneck, from the overall flow, but also as an ingenious solution to reduce energy consumption.

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Garbage in, garbage out: An analysis of HTML text extractors and their impact on NLP performance

Garbage in, garbage out: An analysis of HTML text extractors...

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international conference on control systems and computer science (cscs)

作者： Vlad Dumitru Denis Iorga Stefan Ruseti Mihai Dascalu Computer Science Department University Politehnica of Bucharest Bucharest Romania R&D Department Research Technology Bucharest Romania Interdisciplinary School of Doctoral Studies University of Bucharest Bucharest Romania

Technological advancement has significantly facilitated the research and development of Artificial Intelligence, with particular emphasis on Natural Language Processing (NLP). High-quality data is crucial to achieving success in this area. this aspect becomes particularly important considering the recent widespread adoption of large language models trained on a considerable amount of text from the Internet. this research expands on the issue of data quality in NLP by examining the impact of automated text extraction techniques from HTML on the performance of specific NLP tasks. For this purpose, an empirical evaluation was conducted to assess the efficacy of various automated techniques for HTML text extraction using 300 news articles written in English, Romanian, and French. the evaluation was conducted by comparing the results of the most popular automated text extraction technologies (i.e., “boiler”, “justext”, “newspaper”, “readability”, and “trafilatura”) against the results of human-validated texts. Both extracted texts, automated and human-validated, were subjected to three NLP tasks: named entity recognition, sentiment analysis, and text summarization. Our analysis of the NLP results indicates that text from Romanian online news articles should be extracted with “newspaper”, whereas “trafilatura” should be used for English and French articles, regardless of the NLP task. Overall, our study provides a comprehensive understanding of the performance of the selected technologies for extracting the text of online news articles by language and NLP task.

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Optimizing Road Safety through Intelligent Congestion Management 24

Optimizing Road Safety through Intelligent Congestion Manage...

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24th international Arab conference on Information Technology, ACIT 2023

作者： Samara, Ghassan Elhilo, Alaa Asassfeh, Mahmoud Rajallah Injadat, MohammadNoor Qasem, Mais Haj Alazaidah, Raed Aljaidi, Mohammad Al-Milli, Nabeel Hnaif, Adnan A Zarqa University Faculty of Information Technology Zarqa Jordan Al-Zaytoonah University of Jordan Faculty of Science and It Amman Jordan

ISBN: (纸本)9798350384307

Intelligent transportation systems offer a promising avenue for enhancing road safety by facilitating communication between vehicles and infrastructure. this communication can significantly reduce road fatalities. Addressing congestion resulting from wireless communication between vehicles and infrastructure is a critical aspect of road safety. this paper focuses on effectively controlling this congestion while ensuring constant awareness in vehicles. the proposed technique involves determining an optimal transmission rate based on the current channel conditions, striking a balance between message awareness and congestion. this is achieved using a reinforcement learning and Markov decision process-based machine learning algorithm. Simulation results demonstrate the superiority of applying machine learning algorithms over traditional congestion control strategies. Additionally, future studies should consider incorporating other characteristics such as packet delay and packet loss for a comprehensive analysis. © 2023 IEEE.

关键词： Intelligent Congestion Management Intelligent transportation systems Road Safety VANET

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Exploring the Effect of Autoencoder Based Feature Learning for a Deep Reinforcement Learning Policy for Providing Proactive Help 24th

Exploring the Effect of Autoencoder Based Feature Learning f...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Alam, Nazia Mostafavi, Behrooz Chi, Min Barnes, Tiffany North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9783031363351;9783031363368

Providing timely assistance to students in intelligent tutoring systems is a challenging research problem. In this study, we aim to address this problem by determining when to provide proactive help with autoencoder based feature learning and a deep reinforcement learning (DRL) model. To increase generalizability, we only use domain-independent features for the policy. the proposed pedagogical policy provides next-step proactive hints based on the prediction of the DRL model. We conduct a study to examine the effectiveness of the new policy in an intelligent logic tutor. Our findings provide insight into the use of DRL policies utilizing autoencoder based feature learning to determine when to provide proactive help to students.

关键词： Intelligent Tutoring systems Deep Reinforcement Learning Autoencoder

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international conference on control systems and computer science (cscs)

作者： Raluca Brehar Adrian Groza Ioana Damian George Muntean Simona Delia Nicoară Department of Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Ophthalmology “Iuliu Hatieganu” University of Medicine and Pharmacy Emergency County Hospital Cluj-Napoca Romania

the image classification task is considered solved from the technical perspective: given enough data - in terms of quantity, quality and distribution - machine learning technology is able to classify various medical conditions with high performance metrics. Yet, one disadvantage is that the large majority of approaches directly target to signal the disease without considering at all the running protocols or medical guidelines used by clinicians. We address here the gap between running clinical protocols and solutions provided by computer scientists. Instead of directly target the disease, we aim the identify only the biomarkers that the physicians are looking for. We apply this approach to the age-related macular degeneration (AMD). the task is to identify biomarkers for the AMD condition by segmenting retinal optical coherence tomography (OCT) images. One relevant question regards the choice between feature based techniques versus deep learning methods. We address this question by providing a hybrid original solution that combines deep learning based retinal layer segmentation with feature based image classification used to distinguish between a particular fluid area and its neighbouring retinal areas. In this context we perform a comparative analysis of various semantic segmentation techniques which provide noticeable results for retinal layer segmentation. On the other hand, due to the low representation of some fluids in the considered benchmark dataset, we address and enhance their recognition by texture based region of interest classification. the proposed approach is a step towards developing AI systems in line with running medical protocols.

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Model Predictive control-Based Hybrid Photovoltaic-Battery System for Weak Grid Conditions with Multifunctionality 24

Model Predictive Control-Based Hybrid Photovoltaic-Battery S...

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24th international Middle East Power System conference, MEPCON 2023

作者： Selim, Fatma Aly, Mokhtar Megahed, Tamer F. Shoyama, Masahito Abdelkader, Sobhy M. Electrical Power Engineering Alexandria New Borg El-Arab City21934 Egypt Aswan University Department of Electrical Engineering Aswan81542 Egypt Universidad San Sebastián Facultad de Ingeniería Arquitectura y Diseño Bellavista 7 Santiago Chile Mansoura University Faculty of Engineering Electrical Engineering Department El-Mansoura35516 Egypt Kyushu University Faculty of Information Science and Electrical Engineering 744 Motooka Nishi-Ku Fukuoka819-0395 Japan

ISBN: (纸本)9798350358469

Battery integration with photovoltaic (PV) systems is essential for mitigating their intermittency effects due to the dependency of their output power on weather conditions. the single-stage PV structure can achieve higher efficiency, lower cost, and reduced number of components among existing structures. However, control complexity increases with single-stage PV systems in the case of integrating battery storage systems. the situation becomes more complicated with the weak grid conditions and the required multi-functionalities of PV systems with electrical grids. therefore, this Paper introduces an enhanced control algorithm utilizing the model predictive control (MPC) approach for single-stage hybrid PV-battery systems. the proposed method can rapidly respond to the transients and variations of load, PV output power, and grid requirements. the simulation results show the enhanced transient response, steady-state response, mitigating week grid effects, and satisfying load and grid requirements. © 2023 IEEE.

关键词： Model predictive control

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