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Mediterranean elec.rotechnical Conference (Melec.N)

作者： Diego García-Vega Samuel González-Castillo Francisco D. González-Martínez Jaime García-Martínez Elías F. Combarro Francisco J. Cañadas-Quesada José Ranilla Dept. of Computer Science University of Oviedo Gijón Spain Dept. of Computer Science University of Oviedo Oviedo Spain Dept. of Telecomm. Eng. University of Jáen Jaen Spain

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

Respiratory sound analysis plays a crucial role in the detection and diagnosis of diseases. With advances in digital signal processing and artificial intelligence techniques, the automated classification of these sounds has become an active area of research. The objective of this work is to analyze the behavior of emerging hybrid quantum-classical machine learning models, which combine the robust capabilities of classical learning algorithms with the potential of quantum variational circuits. To accomplish this, we undertake a comparative analysis between Quantum Support Vector Machines and Quantum Neural Networks, juxtaposed with the equivalent classical AI models, employing various signal representation techniques. Based on the experimental results, integrating quantum techniques into the analysis of respiratory sounds enhances the effectiveness of classical methods, particularly in scenarios with limited examples. This is a common scenario in this context, where data availability is typically constrained.

关键词： Support vector machines Training Analytical models Qubit Signal processing algorithms Machine learning Signal representation

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Knowledge Transfer Challeng.s: A Practical Study of Yemen Public Telec.mmunication Corporation 1

Knowledge Transfer Challenges: A Practical Study of Yemen Pu...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Taweel, Sadik Ali, Ghassan Ahmed Alhazbi, Saleh Yousef, Walid Al-Fuhaidi, Belal Albater, Thamer Hezzam, Essa Ghallab, Abdullatif Department of Information Systems University of Science and Technology Sana’a Yemen Faculty of Islamic Technology Universiti Islam Sultan Sharif Ali Brunei Darussalam Brunei Dept. of Computer Science & Eng. Qatar University Doha Qatar Dept. of Information Technology University of Science and Technology Sana’a Yemen Department of Computer Science University of Science and Technology Sana’a Yemen Faculty of Business Open University Malaysia Sana’a Yemen Department of Information Systems Taibah University Medina Saudi Arabia

ISBN: (纸本)9798331533557

The success of organizations today relies heavily on their intellectual capital, as knowledge is a key resource for value creation. Sharing and transferring knowledge enhances its value, embedding it within an organization’s processes and boosting competitiveness. This study focuses on the Yemen public telec.mmunication corporation (YPTC), which has adopted a knowledge-focused management approach. This study aims to explore challeng.s related to knowledge transfer in Yemen’s public sector, where such research is lacking. Using a descriptive and analytical method, data were collected via a questionnaire from 200 employees, yielding a response rate of 88%. The findings indicate a high level of knowledge transfer at YPTC, with individual, organizational, and technological factors identified as significant influences. The key barriers include trust, information technology support, motivation, and social networks. © 2024 IEEE.

关键词： Knowledge Transfer Yemen Public Telec.m

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Work in Progress: AI-Powered eng.neering-Bridging Theory and Practice

arXiv

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arXiv 2025年

作者： Levy, Oz Dikman, Ilya Levy, Natan Winokur, Michael Faculty of Industrial Eng. and Technology Management - HIT Holon Israel dept. Computer Science - HUJI Jerusalem Israel

This paper explores how generative AI can help automate and improve key steps in systems eng.neering. It examines AI’s ability to analyze system requirements based on INCOSE’s "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced eng.neers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI’s ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI’s potential to streamline eng.neering processes and improve learning outcomes. It also highlights the challeng.s and limitations of AI, ensuring its safe and ethical use in professional and academic settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Requirements eng.neering

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Gemini: Graph-based Versatile News Search eng.ne

Gemini: Graph-based Versatile News Search Engine

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Intelligent Systems for Communication, IoT and Security (ICISCoIS), International Conference on

作者： Sharan Babu Srikar Samudrala Viswanath Pulabaigari Y C A Padmanabha Reddy Ch.Madhu Babu Dept. of Computer Science and Eng. B V Raju Institute of Technology NarsapurMedak Telangana India Dept. of Computer Science and Eng. IIIT Sricity Chittoor Andhra Pradesh India

Productivity and time value are always important in today's fast-paced world. It is especially true for tasks like ‘News Search’ where a consumer wants Fast access to events around the world, but in this event, Don't rush, be a fair and eclectic cause. This paper aims to show how the state- of-the-art natural language processing techniques with a graph database can be used to de-duplicate redundant content from data sources and help readers to save time. A graph database uses a data model consisting of nodes, edges, and properties to store and retrieve data. In this paper, A specific schema used in this paper called the hypernode graph where the hyper-nodes connect to nodes that are relevant. These hypernodes are themselves linked by semantics. Similarity assessment offers innovative methods for modeling the News de-duplication problem in a scalable way. This work provides an extensible system that can easily extend for non-news use cases as well.

关键词： Productivity Databases Soft sensors Semantics Search eng.nes Natural language processing Data models

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Tailored Transformations for Constraint Satisfaction in Learning of Parametric Controllers for Nonlinear Systems

Tailored Transformations for Constraint Satisfaction in Lear...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Lukas Markolf Olaf Stursberg Dept. of Electrical Eng. and Computer Science Inst. of Control and System Theory University of Kassel Germany

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The paper on hand addresses the problem of guaranteed satisfaction of ellipsoidal state and input constraints when learning parametric controllers for discrete-time nonlinear but control-affine systems with additive bounded disturbances. The parametric controllers are proposed to be compositions of parametric functions (like neural networks), for which state-dependent transformations lead to ranges that are equal to sets of admissible control inputs derived from offline computed reachable state sets. In this way, constraint satisfaction is guaranteed for all control laws that can be learned through the choice of the parameter vector.

关键词： Linear systems Additives Supervised learning Neural networks Aerospace elec.ronics Control systems Vectors Nonlinear systems Information technology Standards

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Approximations of Optimal Control Laws for Constrained Piecewise Affine Systems by Deep Neural Networks

Approximations of Optimal Control Laws for Constrained Piece...

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European Control Conference (ECC)

作者： Lukas Markolf Arne Siewert Olaf Stursberg Dept. of Electrical Eng. and Computer Science Inst. of Control and System Theory University of Kassel Germany

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

The paper on hand considers the optimal control of piecewise affine systems subject to polytopic constraints. While this problem can be addressed by receding horizon control, the approach is known to be computationally demanding. This paper considers the approximation of receding horizon control laws by deep artificial feed-forward neural networks. The concept of projecting inadmissible inputs onto regions derived from feasible sets is extended to the considered problem setup in order to achieve deterministic guarantees on feasibility and constraint satisfaction. Two approaches are proposed and illustrated in numerical examples.

关键词： Linear systems Optimal control Training data Artificial neural networks Power system stability Network architecture Stability analysis

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Enhancing DDoS Attack Detection: A Hybrid SVM-Decision Tree Ensemble Approach

Enhancing DDoS Attack Detection: A Hybrid SVM-Decision Tree ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Debendra Muduli Sandesh Bhatta Shantanu Shookdeb Aayush Adhikari Aaron Sapkota Prabhat Chaturvedi Dept. of Computer Science and Eng. C. V. Raman Global University Bhubaneswar India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

A DDoS attack overwhelms a network or server with an excessive amount of traffic, disrupting its normal operation and rendering it inaccessible to legitimate users. DDoS attacks involve coordinated efforts from multiple compromised devices, known as botnets, flooding the target with traffic simultaneously. This flood of data exhausts the target’s resources, causing service disruptions. Machine learning aids in DDoS attack detection by analyzing network traffic patterns. It learns to differentiate between normal and malicious activity by studying features such as packet rates, traffic volume, and protocol anomalies. By training on labeled datasets containing both benign and attack traffic, machine learning algorithms can identify deviations indicative of DDoS attacks, thereby enhancing network security. The hybrid machine learning model combines the precision of Support Vector Machines with the decision-making clarity of Decision Trees to effectively combat DDoS attacks. This innovative model achieves an impressive accuracy rate of $\mathbf{9 9. 9 8 \%}$ against the CICDDoS2019 dataset, demonstrating its exceptional performance in detecting various DDoS attack vectors.

关键词： Support vector machines Training Adaptation models Machine learning algorithms Accuracy Decision making Denial-of-service attack Numerical models Floods Decision trees

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Predictive Performance of Novel Machine Learning Model For Liver Disease Detection Using Ensemble Technique

Predictive Performance of Novel Machine Learning Model For L...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Aman Kushwaha Sukant Kishoro Bisoy Swayam Prasad Sahoo Sudhanshu Kumar Girijesh Kumar Rajak Dept. of Computer Science and Eng. C. V. Raman Global University Bhubaneswar India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Liver disease remains a leading cause of death worldwide, making early diagnosis crucial for improving patient outcomes. Clinical trials for liver disease detection can be expensive and time-consuming. In this paper an ensemble method is proposed for efficient detection of liver disease prediction. Utilizing a dataset that includes critical parameters like overall bilirubin, conjugated bilirubin, and alkaline phosphatase, we evaluated several models: XGBoost, AdaBoost, LR, KNN, and feedforward neural networks (FNN) and for ensemble model we have used bagging techniques. Our findings indicates that the proposed ensemble model performs better than other existing state of art methods and significantly outperforms them in terms of predictive performance. This approach shows an accuracy of 95.31 % accuracy thus promises as a powerful tool for the early detection of liver diseases, including hepatitis, potentially leading to improved patient outcomes.

关键词： Accuracy Embedded systems Liver diseases Predictive models Signal processing Nearest neighbor methods Clinical trials Feedforward neural networks Ensemble learning Bagging

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Defining Requirements Strategies in Agile: A Design science Research Study

arXiv

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arXiv 2024年

作者： Muhammad, Amna Pir Knauss, Eric Batsaikhan, Odzaya El Haskouri, Nassiba Lin, Yi-Chun Knauss, Alessia Dept. of Computer Science and Eng. Chalmers | University of Gothenburg Gothenburg Sweden Zenseact AB Gothenburg Sweden

Research shows that many of the challeng.s currently encountered with agile development are related to requirements eng.neering. Based on design science research, this paper investigates critical challeng.s that arise in agile development from an undefined requirements strategy. We explore potential ways to address these challeng.s and synthesize the key building blocks of requirements strategies. Our design science research rests on a multiple case study with three industrial cases in the domains of communication technology, security services, and automotive. We relied on a total of 20 interviews, two workshops, participant observation in two cases, and document analysis in each of the cases to understand concrete challeng.s and workflows. In each case, we define a requirements strategy in collaboration with process managers and experienced eng.neers. From this experience, we extract guidelines for defining requirements strategies in agile development. © 2024, CC BY.

关键词： Requirements eng.neering

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Mentorship and Legitimate Peripheral Participation in the Research Laboratory

Mentorship and Legitimate Peripheral Participation in the Re...

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Frontiers in Education (FIE) Conference

作者： Glen Hordemann Francis Quek Dept. of Electrical Eng. and Computer Science Texas A&M University College Station TX USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research-to-practice paper investigates how undergraduates organically transition into researchers through the lens of Legitimate Peripheral Participation, Legitimate Peripheral Participation is a contextual learning phenomenon through which new members become core members of a community. These peripheral members begin with the execution of peripheral low-risk and simple activities, gradually accruing knowledge and experience as they take on more complex and essential activities. They gain legitimacy and access to more complex involvement through interaction with, and mentorship by, senior members of the community. Our research examines how seven undergraduate students, who are involved with a research lab, transition from peripheral roles to core membership through observation and peripheral eng.gement, skill development, community integration, increasing responsibility, and collaborative projects. These undergraduates interact with and are mentored by both graduate students and faculty members in an organic fashion, with no assigned mentorship roles existing between the undergraduate students and graduate students or faculty. The focus of our research is not on how one recruits undergraduates to work in a research lab, but on how the identity of an undergraduate as a researcher, both in their own mind and in those of their coworkers, is nurtured and developed. Through the process of surveys and interviews we extract the core elements of the undergraduates' experience and explore factors that might contribute to or hinder their transition into being more active researchers. The degree of involvement and identification as a researcher varied significantly between the undergraduate students. The key elements we discover at social eng.gement, physical presence, and near-peer interaction.

关键词： Surveys Publishing IEEE Senior Members Collaboration Logic gates Interviews Lenses

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