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Enhancing Classification Interpretability with Argument-Based Explanations 16

Enhancing Classification Interpretability with Argument-Base...

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16th international conference on ENTERprise Information Systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Possebom, Ayslan T. Sanches, Hugo E. Aylon, Linnyer B.R. Federal Institute of Paraná 1400 José Felipe Tequinha St. ParanavaíPR 87703-536 Brazil State University of Maringá C56 5790 Colombo Ave MaringáPR 87020-900 Brazil

In computational argumentation theory, an argument is composed of a set of premises that collectively support a conclusion. An argument is often employed in a reasoning process where the internal structure can justify or explain decisions in real-world scenarios. When arguments participate in an argumentation process, the reasoning involves complex, contradictory, or incomplete information. In this paper, we introduce a method for extracting potentially essential arguments from consistent datasets. these arguments serve to explain the classifications produced by supervised machine learning algorithms by identifying the sets of premises that justify the predictions. We applied this method to five datasets, demonstrating how the generated arguments can effectively elucidate classification outcomes. the results showed that our approach can identify the relevant features that explain the possible reasons for the classification. Our findings highlight the method's capability to enhance the interpretability of machine learning models by providing straightforward, clear, and premise-based explanations for their outcomes. © 2025 the Author(s).

关键词： Contrastive learning

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Building an English learning Community Model Assisted by AI and VR Technologies

Building an English Learning Community Model Assisted by AI ...

引用

2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Zhu, Guangzhen Xi'an Fanyi University Shaanxi province Xi'an710105 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

With the further promotion of information technology in universities, the continuous enrichment of online teaching resources, and the continuous standardization of online teaching management, online learning as a main learning method has been increasingly accepted by more and more people. this article selects fuzzy neural networks for modeling and constructs a network learning evaluation system based on the characteristics of network learning evaluation. the English teaching classroom based on artificial intelligence and virtual reality proposed in this article can provide scarce educational resources while improving students' learning outcomes. the experimental results show that the method proposed in this article has a higher level of acceptance among students compared to traditional teaching methods, with a 12.7% higher learning efficiency among students. It is feasible in theory and reliable in practice, and can be used as an intelligent computing model for developing network learning evaluation systems. It has good promotional value and provides a new evaluation method for network learning evaluation. © 2025 SPIE.

关键词： Federated learning

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Digital learning Space in Universities from the Perspective of Agglomeration Advantage theory 14th

Digital Learning Space in Universities from the Perspective ...

引用

14th international conference on Frontier Computing, FC 2024

作者： Jiang, Chi School of Automation Wuhan University of Technology Hubei Wuhan China

ISBN: (纸本)9789819627974

With the rapid development of computers and information technology, e-learning has become one of the main ways for people to live, work, and learn. In this situation, traditional classroom teaching can no longer meet people's demand for knowledge. the rise of electronic learning has greatly improved the efficiency of students’ extracurricular learning, made up for the shortcomings of traditional classrooms, achieved lifelong learning, and promoted education for all. Electronic learning (E-learning), due to its abundant resources, diverse forms, and unconstrained time and space, creates fertile soil and growth space for learners to engage in self-directed learning. Simply put, E-learning is a new mode of online teaching, which involves building an internet platform within the scope of teaching, allowing students to learn through the internet. Its architecture includes a learning management system, a content management system, a learning objective library, and a virtual learning community. learning usually occurs in specific time and space, and learning temporal dynamics is an important direction of learning research. However, with the arrival and development of the digital age, the connotation and form of educational space have also changed, and have been pushed to a new level. Research shows that 75% of college students tend to use digital learning environments more, indicating a higher acceptance of digital learning. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Trust Forge: Harnessing Machine learning to Build Trust on Social Networks 14th

Trust Forge: Harnessing Machine Learning to Build Trust on S...

引用

14th EAI international conference on Broadband Communications, Networks, and Systems, BROADNETS 2024

作者： Chitrala, Kavitha Makka, Shanthi Sowjanya, S. Vardhaman College of Engineering Telangana Kacharam Shamshabad Hyderabad501218 India Computer Science and Engineering Vardhaman College of Engineering Telangana Kacharam Shamshabad Hyderabad501218 India

ISBN: (纸本)9783031811708

A social media platform is a form of service offered by an online platform that facilitates easy communication between individuals, as well as the establishment of interpersonal connections and social exchanges. Additionally, it supplies users with a webpage where they may create an open persona and engage adding additional users. Trust is a significant concern in social networking sites, and to address this issue, we have employed the Naive Bayes algorithm to establish trust in online networks. this algorithm is implemented through direct and indirect communication, and trust values are calculated using Dempster-Shafer theory and Bayesian conditional. the effectiveness of our proposed approach is demonstrated through the reenactment results obtained with various parameter arrangements. "In summary, our comparison demonstrates that Multi-faceted trust modeling is statistically and significantly superior to Naive Bayes Model in addressing based on accuracy." © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Adversarial machine learning

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Analysis of Joint Failure Pattern of Reinforced Concrete Special-Shaped Column Based on Neural Network and Interpretable Machine learning 14th

Analysis of Joint Failure Pattern of Reinforced Concrete Spe...

引用

14th international conference on Frontier Computing, FC 2024

作者： Ji, Yongjie Bai, Yuehua China United Engineering Co. Ltd. Hangzhou310052 China School of Management Wuhan Donghu University Hubei Wuhan430212 China

ISBN: (纸本)9789819627974

the purpose of this study is to make use of machine learning theory to deeply study the mechanical performance of steel reinforced concrete special-shaped column joints by data driven instead of the traditional mechanism driven method. In this study, the node data set of reinforced concrete special-shaped column containing several key parameters was established, and the improved neural network algorithm was used to construct the prediction model, and a high precision of the bearing capacity prediction was obtained. through the combination of experimental data and computer vision model, the formation process and failure form of joint cracks are analyzed, and an image processing method without human intervention is proposed, which can effectively reduce the error. Based on explainable machine learning theory, we explain and analyze the factors that affect the bearing capacity of special-shaped column joints, and reveal the role of key factors in the bearing capacity of joints. this research combines neural network, computer vision and interpretable machine learning to provide comprehensive technical support and in-depth explanation for seismic design of reinforced concrete special-shaped column joints. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Seismic design

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Efficacy and Enjoyment in Blended Professional Training for Skeptical Trainees 14th

Efficacy and Enjoyment in Blended Professional Training for ...

引用

14th international conference on Methodologies and Intelligent Systems for Technology Enhanced learning, mis4TEL 2024

作者： Torre, Sara Ligorio, Maria Beatrice Università degli studi di Bari "Aldo Moro" Bari Italy

ISBN: (纸本)9783031855603

Even if Blended learning (BL) is often utilized to program efficient professional training, trainees may still resist this method for several reasons. In the case described in this study, 18 professionals (mean age 55,7 years;8 females - 10 males) were skeptical about BL based on previous experiences and because of the feelings of isolation lived during the lock-down of their offices. Following the directions given by the variation theory, our BL program included a variety of training activities aimed to engage trainees and overcome their skepticism. Participants were asked to test their knowledge with a general test, and they could self-assess the efficacy and enjoyment of each training activity. Data was collected during the whole training program and later analyzed to test the efficacy of the entire training program and training activities were compared to detect the most enjoyable and effective one. Results show a clear preference for face-to-face lessons and group activities, especially when compared to remote lessons. Conclusions are drawn about the necessity of integrating a traditional approach to teaching in BL mode. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Machine learning Approaches for the Early Diagnosis of Autism Spectrum Disorder: A Systematic Framework 15th

Machine Learning Approaches for the Early Diagnosis of Autis...

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15th international conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Narayanan, Neethu Remya, K.R. Varghese, Bindiya M. Department of Computer Science Rajagiri College of Social Sciences (Autonomous) Kerala Kochi India

ISBN: (纸本)9783031810824

this study uses machine learning to address the urgent need for early detection and intervention in autism spectrum disorder (ASD). Autism Spectrum Disorder (ASD), a disorder of neural development affecting cognition and social interaction, necessitates customized interventions. Our research uses various screening assessments in early infancy as datasets to train for neural network algorithms, with the objective of identifying patterns associated with ASD. the suggested methodology utilizes predictive machine learning techniques to assess the ASD spectrum, accounting for its diverse pitches. Utilizing sophisticated screening instruments and an innovative methodology, we seek to establish a dependable system for the early detection of ASD. Ethical considerations, data privacy, and algorithmic openness are essential. the studies seek to foster collaboration among experts in neurodevelopment, psychological science, and data science to build precise tools for earlier identification of ASD. the use of machine learning, an effective tool, enhances the knowledge of healthcare workers. the research results underscore technology's capacity to transform initial measures, leading to enhanced outcomes for those on the autistic spectrum. It provides this assistance to enhance the dialogue at the gathering, thereby promoting interaction at the nexus of healthcare and digital technology. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Autism Spectrum Disorder (ASD) Machine learning Neurological

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Predictive Analytics in Higher Education: A Systematic Literature Review of Machine learning Models for Forecasting Academic Performance and Ethical Integration

Predictive Analytics in Higher Education: A Systematic Liter...

引用

international conference on Educational and Information Technology (ICEIT)

作者： Alex N. Remegio College of Engineering Sultan Kudarat State University Isulan Mindanao Philippines

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

this systematic literature review examines predictive analytics and machine learning (ML) applications in edu-cational settings from 2019 to 2023. Machine learning (ML) algorithms are increasingly used to predict students' academic performance and are integral to early warning systems in educational settings. the study looks at several algorithms, from simple linear regression to more complex ensemble and neural network models. It also looks at how well they work in different types of schools and with different types of students. the analysis confirms that ML models provide educators with actionable insights to identify and support at-risk students. Along with evaluating predictive performance, the review discusses the ethical problems of using machine learning in education. these include worries about data privacy, the need for openness, and the possibility of bias in algorithmic decision-making. the paper concludes that while ML holds substantial promise in enhancing educational outcomes, its deployment requires prudent management to navigate the intersection of technological innovation and ethical practice, ensuring responsible use of data analytics in shaping the future of education.

关键词： Ethics Machine learning algorithms Education Neural networks Machine learning Predictive models Prediction algorithms Forecasting Predictive analytics Systematic literature review

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14th international conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2025, held as part of EvoStar 2025

14th International Conference on Artificial Intelligence in ...

引用

14th international conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2025, held as part of EvoStar 2025

ISBN: (纸本)9783031901669

the proceedings contain 28 papers. the special focus in this conference is on Artificial Intelligence in Music, Sound, Art and Design. the topics include: Search-Based Negative Prompt Optimisation for Text-to-Image Generation;Exploring the Application of AIGC in Ink-Wash Animation Creation:A Case Study of Dragon Gate;AI in Music and Healthcare: A Comparative Survey;combining Local Search and Directed Mutation in Evolutionary Approaches to 4-Part Harmony;exploiting the Temporal Order of Sound Features for Onset Detection;towards Human-Quality Drum Accompaniment Using Deep Generative Models and Transformers;an Ensemble Approach to Music Source Separation: A Comparative Analysis of Conventional and Hierarchical Stem Separation;balancing Indeterminacy and Structure: Neural Text Generation for Artistic Inspiration;Exploring Bridges Between algorithmic and AI-Generated Art;future Sight: Fine-Tuning Language Models for Dynamic Story Generation;All YIN No YANG: Geometric Abstraction of Oil Paintings with Trained Models, Noise and Self-reference;exploring Multi-objective Evolution for Aesthetic and Abstract 3D Art;music Similarity through Geometric Overlap;graph Neural Network vs Feature-Based Folk Music Evolution Analysis;Generating Virtual Landscapes and Environmental Narratives with StyleGAN2;towards the Automatic Evaluation of Legibility for Graphic Design Posters;short Video Interestingness: A Machine learning Approach to Determine Creative Cues in Audiovisual Production;automated Selection and Ordering of Clip Sequences for Music Videos Based on Tonal Tension and Visual Features;evolving the Embedding Space of Diffusion Models in the Field of Visual Arts;steering Large Text-to-Image Model for Kandinsky Synthesis through Preference-Based Prompt Optimization;emotioNotes Dataset: Decoding Emotions in Classical Music through Concert Program Notes;aesthetic Biases and Opacity Tactics in the Training of Visual Artificial Intelligence Models;yin-Yang: Developing Motifs with L

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Bridging theory and Practice in Outcome-Based Assessment: Developing a Web Application for learning Outcome Alignment

Bridging Theory and Practice in Outcome-Based Assessment: De...

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international conference on Educational and Information Technology (ICEIT)

作者： Abdulsalam K. Alhazmi Faculty of Engineering & Computing University of Science and Technology Aden Yemen

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

Assessment remains a cornerstone of the educational process, with standardized testing often serving as a primary method for evaluating learning. However, as pedagogical approaches continue to evolve, Outcome-Based Assessment (OBA) has gained prominence as a more comprehensive model that aligns learning outcomes with teaching strategies. this paper explores the significance of OBA, grounded in frameworks of domain learning, and highlights its global adoption as a measure of institutional quality. By focusing on measurable learning outcomes across cognitive domains, OBA enhances student engagement, critical thinking, and skill development. Extending this theoretical foundation, the paper presents an Outcome Based Assessment System (OBAS) developed to facilitate the practical implementation of OBA. OBAS provides a structured framework that links program learning outcomes to course learning outcomes, integrating considerations of learning domains and difficulty levels. By supporting the creation of standardized and authentic OBA assessments, this application empowers educators and institutions to uphold educational quality, foster accountability, and align assessments with targeted learning objectives.

关键词： Industries Bridges Education Employment Focusing learning (artificial intelligence) Benchmark testing Prediction algorithms Classification algorithms Information technology

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