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11th International Conference on Electrical Engineering, computer science and Informatics, EECSI 2024

作者： Hermawan, Latius Stiawan, Deris Syakurah, Rizma Adlia Meilinda Ikhsan, Diyaz Syauki Universitas Sriwijaya Faculty of Science and Technology UKMC / Faculty of Engineering Palembang Indonesia Universitas Sriwijaya Computer Engineering Department Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Public Health Study Departement Faculty of Public Health Palembang Indonesia Universitas Sriwijaya Biology Education Departement Faculty of Teacher Training and Science Education Palembang Indonesia Universitas Sriwijaya Medical Education Departement Faculty of Medical Palembang Indonesia

ISBN: (纸本)9798350355314

Academic mental health is crucial for university students as it directly influences their academic performance, personal well-being, and future professional development. Factors such as academic stress, social isolation, financial difficulties, and family expectations can negatively impact students' mental health. Early identification of these risk factors is essential for developing effective interventions to prevent more severe mental health issues. The development of a prediction-based intervention chatbot using a machine learning approach offers a promising solution. This research utilizes a dataset derived from the DASS-21 scale, which measures levels of depression, anxiety, and stress, comprising 424 records of question-and-answer variations to train the chatbot. The dataset was divided into training and testing sets with an 80:20 ratio, allowing for unbiased model evaluation. The study compared the performance of two machine learning models: Random Forest Classifier (RFC) and Multinomial Naive Bayes (MNB). Results showed that RFC outperformed MNB, achieving an accuracy of 82.5%, along with high precision, recall, and F1-score, demonstrating its effectiveness in identifying relevant conversations and providing appropriate interventions. Although MNB is faster and more efficient, its lower accuracy of approximately 66% presents a risk of inaccurate responses. These findings highlight the potential of using RFC in a mental health intervention chatbot to offer reliable and high-quality support, making a significant contribution to both mental health and computer science by providing an effective tool for detecting and treating mental health issues among university students. © 2024 IEEE.

关键词： Electronic health record

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Fog-Based Traffic Lights and Intelligent Control Using Fuzzy Logic and Recurrent Neural Networks to Mitigate Vehicle Queues in Latin American Colonial Environments 41

Fog-Based Traffic Lights and Intelligent Control Using Fuzzy...

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41st IEEE Central America and Panama Student Conference, CONESCAPAN 2024

作者： Sandoval, Estuardo Ramos, José Andrés Conde, Luis Serrano Universidad de la Rioja Department of Computer Science Logroño Spain Universidad Del Istmo Faculty of Engineering Fraijanes Guatemala

ISBN: (纸本)9798350377590

Intelligent traffic management systems have emerged as a promising solution to address the increasing traffic congestion in Latin American cities, where rapid urban population growth and limited infrastructure capacity have led to significant mobility challenges. This paper proposes an innovative traffic management system, IOTL (Internet of Traffic Lights), which integrates fuzzy logic and recurrent neural networks (RNN) to adaptively control traffic lights and mitigate vehicular queues. The system architecture is designed to address the specific challenges of Latin American cities, such as the non-uniformity of intersection distribution and the reliability issues of wireless internet connections. The methodology encompasses system design, traffic simulation using SUMO, data collection and processing, system implementation, and validation. Simulation results demonstrate significant reductions in queue lengths compared to pure fuzzy logic techniques and uncontrolled methods. The importance of adaptive control in mitigating urban congestion is highlighted, with the combination of fuzzy logic's reactivity and RNN's proactivity offering an efficient solution. The proposed system has the potential to significantly improve urban mobility and quality of life in Latin American cities. Future research should focus on real-world validation, algorithm optimization, scalability and generalization, integration with other ITS components, and long-term impact assessment. © 2024 IEEE.

关键词： Recurrent neural networks

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Machine Learning-Powered Intrusion Detection for Network Security 12th

Machine Learning-Powered Intrusion Detection for Network Sec...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Nandish, H.D. Rahul, R. Dasari, Hari Charan Kumaran, U. Umah, B. Nagarajan, Senthil Murugan Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Mathematics Faculty of Science Technology and Medicine University of Luxembourg Esch-sur-AlzetteL-4365 Luxembourg

ISBN: (纸本)9789819788354

In today's digital age, network security is paramount. This paper introduces a robust web application designed to swiftly identify network attacks and real-time anomalies, enabling users to protect their networks effectively. By leveraging cutting-edge technologies and machine learning, our system provides insights into network accuracy, F1-score, and precision, allowing users to gauge their network's safety. Our intrusion detection system classifies attacks into four categories: Denial of Service (DoS), Probe, Remote-to-User (R2L), and User-to-Root (U2R). Positioned strategically within the network, it monitors data traffic from all connected devices and promptly responds to suspicious activity, ensuring network security. Users receive instant alerts to take immediate action. Key technologies include data preprocessing (LabelEncoder and MinMaxScaler), user-friendly web interface (MERN stack), and enhanced security with Google Earth 2.0 for authentication and MongoDB for secure data storage. The Python-based backend operates on the NSL-KDD dataset, facilitating effective intrusion detection evaluation. To enhance accuracy, our system employs four machine learning algorithms: K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), Random Forest, and Long Short-Term Memory (LSTM), offering comprehensive protection against network threats. In summary, this paper presents a potent network intrusion detection system for real-time threat identification and security assessment. By combining advanced technologies and machine learning, it ensures accuracy and reliability, serving as a valuable tool for network administrators and security professionals in safeguarding digital networks from evolving threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Understanding Interpersonal Conflict Types and their Impact on Perception Classification 5

Understanding Interpersonal Conflict Types and their Impact ...

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5th Workshop on Natural Language Processing and Computational Social science, NLPCSS 2022, Held at the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Welch, Charles Plepi, Joan Neuendorf, Béla Flek, Lucie Lab Department of Mathematics and Computer Science University of Marburg Germany Germany

ISBN: (纸本)9781959429203

Studies on interpersonal conflict have a long history and contain many suggestions for conflict typology. We use this as the basis of a novel annotation scheme and release a new dataset of situations and conflict aspect annotations. We then build a classifier to predict whether someone will perceive the actions of one individual as right or wrong in a given situation. Our analyses include conflict aspects, but also generated clusters, which are human validated, and show differences in conflict content based on the relationship of participants to the author. Our findings have important implications for understanding conflict and social norms. © 2022 Association for Computational Linguistics.

关键词： Classification (of information)

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Mixed Reality Application in Robot Navigation and Control 16

Mixed Reality Application in Robot Navigation and Control

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16th International Conference on Applied and Theoretical Electricity, ICATE 2024

作者： Cismaru, Stefan-Irinel Petcu, Florina-Luminita Resceanu, Ionut-Cristian Roibu, Horatiu Trasculescu, Andrei-Costin Pana, Cristina-Floriana Bizdoaca, Nicu-George University of Craiova Faculty of Automation Computers and Electronics Mechatronics and Robotics Department Craiova Romania University of Craiova Faculty of Automation Computers and Electronics Computer Science Department Craiova Romania

ISBN: (纸本)9798350388107

Among the emerging technologies, Mixed Reality (MR) has provided the means to interact with holograms. A very distant future is now near and accessible, which allows for the replacement of classic controllers for robots and is driven by MR. Holograms involve dynamic 3D elements that are integrated into the environment, enhancing control means for the user. These elements may be visually attended to and interacted with via eye concentration or hand gestures, and they have the capability to execute a variety of activities. This emerging technology enables interactions that are otherwise unattainable in alternative settings or contexts, such as the presence of an avatar inside the program. Furthermore, it has the potential to be expanded to govern other mechatronic devices, including robots. The purpose of this work is to offer an improved and interactive method of controlling a mechatronic platform by using holograms, such that the adaptability of this technology may be shown in the context of operating real systems. Important for the authors of this paper is the broadening of the spectrum in which the application lies, thus contributing to the process followed for the development and applicability of the solution in key areas such as industry, training, and medicine. © 2024 IEEE.

关键词： Holograms

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Damage Assessment and Recovery in Fog-based Computing Systems

Damage Assessment and Recovery in Fog-based Computing System...

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2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2022

作者： Thoumi, Sergio Haraty, Ramzi A. Lebanese American University Department of Computer Science and Mathematics Beirut Lebanon

ISBN: (数字)9781665451932

ISBN: (纸本)9781665451932

Technological improvements have been exponential in the last two decades. The advancements in networking, fog computing, and artificial intelligence have allowed us to implement the "Internet of Things"starting from small sensors connected to the Internet to enormous "smart cities". However, what is common in all of these improvements is that without data, and more precisely correct data, they would be useless. For example, machine learning training and results will be useless once a malicious or affected transaction is done because it can spread throughout the entire database. The need for data integrity made scholars research different phases of data security such as detection, prevention, and recovery. In this paper, we present an effective damage assessment and recovery algorithm in fog based systems that shortens crucial downtime caused by the recovery process after an attack. © 2022 IEEE.

关键词： Fog computing

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Generalized Parabola Chaotic map for Pseudorandom Random Number Generator 26

Generalized Parabola Chaotic map for Pseudorandom Random Num...

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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Jiteurtragool, Nattagit King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Sciences Bangkok10800 Thailand

ISBN: (纸本)9791188428120

In this paper, a generalized form of chaotic map based on nonlinear function with parabolic shape is introduced. The study involves the investigation of chaotic dynamics in terms of apparent in time-domain, and both qualitatively and quantitatively examination using bifurcation diagram and Lyapunov exponents. Furthermore, the practical application of these parabolic chaotic maps is showcased in a pseudorandom number generator, with its performance evaluated using statistical tests from the NIST SP800-22 test suite. © 2024 Global IT Research Institute - GIRI.

关键词： Lyapunov methods

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Property Testing of Curve Similarity

arXiv

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arXiv 2025年

作者： Afshani, Peyman Buchin, Maike Driemel, Anne Richter, Marena Wong, Sampson Department of Computer Science Aarhus University Denmark Faculty of Computer Science Ruhr University Bochum Germany Institute for Computer Science University of Bonn Germany Department of Computer Science University of Copenhagen Denmark

We propose sublinear algorithms for probabilistic testing of the discrete and continuous Fréchet distance—a standard similarity measure for curves. We assume the algorithm is given access to the input curves via a query oracle: a query returns the set of vertices of the curve that lie within a radius δ of a specified vertex of the other curve. The goal is to use a small number of queries to determine with constant probability whether the two curves are similar (i.e., their discrete Fréchet distance is at most δ) or they are ‘Ε-far’ (for 0 t/Ε log t/Ε ) queries and is given the value of t in advance. The second algorithm does not have explicit knowledge of the value of t and therefore needs to gain implicit knowledge of the straightness of the input curves through its queries. We show that the discrete Fréchet distance can still be tested using roughly O(t3 +t2 log n/Ε) queries ignoring logarithmic factors in t. Our algorithms work in a matrix representation of the input and may be of independent interest to matrix testing. Our algorithms use a mild uniform sampling condition that constrains the edge lengths of the curves, similar to a polynomially bounded aspect ratio. Applied to testing the continuous Fréchet distance of t-straight curves, our algorithms can be used for (1 + Ε′)-approximate testing using essentially the same bounds as stated above with an additional factor of poly(1/Ε′ ). Copyright © 2025, The Authors. All rights reserved.

关键词： Approximation algorithms

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Community Clustering on Fraud Transactions Applied the Louvain-Coloring Algorithm

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International Journal of Electronics and Telecommunications 2023年第3期69卷 593-598页

作者： Mardiansyah, Heru Suwilo, Saib Nababan, Erna Budiarti Efendi, Syahril Department Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia Department Data Science and Artificial Intelligence Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

The contribution main from this research is modularity and better processing time in detecting community by using K-1 coloring. Testing performed on transaction datasets remittance on P2P platforms where the Louvain Coloring algorithm is better in comparison to Louvain Algorithm Data used is data transfer transactions made by customers on the P2P Online platform. The data is the User data that has information transfer transactions, Card data that has information card, IP data that has IP information, and Device data that has information device. Every user owns unique 128-bit identification, and other nodes representing card, device, and IP are assigned a random UUID. The Device node has the guide, and device properties. IP nodes only have property guide and node User has property fraud Money Transfer, guide, money Transfer Error Cancel Amount, first Charge back Date. Each node has a unique 128-bit guide, with the amount whole of as many as 789,856 nodes. Application technique K-1 staining on Louvain algorithm shows enhancement value modularity and better processing time for detecting community on the network large scale. Through a series of exercises and tests carried out in various scenarios, it shows that the experiments carried out in this paper, namely the Louvain Coloring algorithm, are more effective and efficient than the Louvain algorithm in scenario 1,3, and 5 meanwhile For Scenarios 2 and 4 Louvain Algorithm is better. © 2023 Polish Academy of sciences. All rights reserved.

关键词： Clustering algorithms

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The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track 38

The State of Data Curation at NeurIPS: An Assessment of Data...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bhardwaj, Eshta Gujral, Harshit Wu, Siyi Zogheib, Ciara Maharaj, Tegan Becker, Christoph Faculty of Information University of Toronto Canada Department of Computer Science University of Toronto Canada Digital Curation Institute University of Toronto Canada

Data curation is a field with origins in librarianship and archives, whose scholarship and thinking on data issues go back centuries, if not millennia. The field of machine learning is increasingly observing the importance of data curation to the advancement of both applications and fundamental understanding of machine learning models - evidenced not least by the creation of the Datasets and Benchmarks track itself. This work provides an analysis of recent dataset development practices at NeurIPS through the lens of data curation. We present an evaluation framework for dataset documentation, consisting of a rubric and toolkit developed through a thorough literature review of data curation principles. We use the framework to systematically assess the strengths and weaknesses in current dataset development practices of 60 datasets published in the NeurIPS Datasets and Benchmarks track from 2021-2023. We summarize key findings and trends. Results indicate greater need for documentation about environmental footprint, ethical considerations, and data management. We suggest targeted strategies and resources to improve documentation in these areas and provide recommendations for the NeurIPS peer-review process that prioritize rigorous data curation in ML. We also provide guidelines for dataset developers on the use of our rubric as a standalone tool. Finally, we provide results in the format of a dataset that showcases aspects of recommended data curation practices. Our rubric and results are of interest for improving data curation practices broadly in the field of ML as well as to data curation and science and technology studies scholars studying practices in ML. Our aim is to support continued improvement in interdisciplinary research on dataset practices, ultimately improving the reusability and reproducibility of new datasets and benchmarks, enabling standardized and informed human oversight, and strengthening the foundation of rigorous and responsible ML research. © 2

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