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共 107108 条记录，以下是341-350 订阅

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Vibration-Based Failure Prediction in Machinery: Novel Application of Time Series Decomposition and Neural Networks 5

Vibration-Based Failure Prediction in Machinery: Novel Appli...

引用

5th international conference on Advanced Research in computing, ICARC 2025

作者： Nirmal, W.C. Lakmal, H.K.I.S. Fernando, K.J.P. Nsbm Green University Faculty of Engineering Department of Mechatronic & Industrial Engineering Homagama Sri Lanka Nsbm Green University Faculty of Engineering Department of Electrical Electronic & Systems Engineering Homagama Sri Lanka

ISBN: (纸本)9798331530983

Predictive maintenance (PdM) is critical in reducing downtime and minimizing operational costs in industrial settings. This study introduces a novel framework for vibration-based failure prediction by combining Seasonal and Trend Decomposition using LOESS (STL) and deep learning models, specifically Long Short-Term Memory (LSTM) networks. The framework processes multivariate vibration data collected from two-roller mills in a tire manufacturing facility. STL is employed to decompose the data into trend, seasonal, and residual components, enabling noise reduction and enhanced feature extraction. These components serve as inputs to LSTM models for predicting machine condition scores. A comprehensive analysis compared five deep learning architectures: LSTM, Bidirectional LSTM, Gated Recurrent Unit (GRU), Deeper LSTM, and Conv1D LSTM. The GRU and Conv1D LSTM demonstrated superior performance across multiple vibration axes, achieving low errors and high R2 values. Results highlight the effectiveness of STL in preparing vibration data for sequential learning and the robust predictive capabilities of deep learning models. This study provides a scalable approach to PdM, enhancing reliability and efficiency in industrial machinery maintenance. Future work includes extending the framework to other machinery types and real-time monitoring systems. © 2025 IEEE.

关键词： Rollers (machine components)

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Research on the Construction Path of teaching Workshops from the Perspective of Structural-Functionalism Theory 14th

Research on the Construction Path of Teaching Workshops from...

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14th international conference on Frontier computing, FC 2024

作者： Wu, Jun Huang, Xiujuan Liu, Hsiao-Fen National Taipei University of Technology No. 81 Section 1 Jianguo South Road Da’an District Taipei City106 Taiwan

ISBN: (纸本)9789819623945

The construction of digital teaching factories is a critical component in the digital transformation of vocational education reform and the advancement of the "one body, two wings" strategic initiative. Based on this, using structural functionalism as a theoretical perspective and Parsons’ AGIL model, this study analyzes the importance and practical issues of digital teaching factories in vocational schools. It points out that the construction of digital teaching factories in vocational schools is not merely a matter of technological application but also a significant innovation in educational concepts and methods. This requires deep integration and collaboration among schools, enterprises, and society, strengthening the integration of curriculum systems, teaching methods, and digital technologies, and emphasizing the cultivation and updating of the teaching workforce. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Apprentices

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Performance Evaluation of Machine learning Models in Heart Stroke Prediction 5

Performance Evaluation of Machine Learning Models in Heart S...

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5th international conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Garima Rani, Amita Singhrova, Anita Kumar, Sanjay Dcrust Computer Science and Engineering Department Murthal Sonepat India Sgt University Computer Science and Engineering Department Gurugram India

ISBN: (纸本)9798331522100

Stroke, a persistent medical condition with a rising yearly occurrence, can be triggered by the sudden interruption of blood flow to a section of the brain or heart. Early symptom detection can provide valuable information for prognosticating stroke and promoting healthy lifestyles. This research proposes guidelines for developing effective machine learning-based methods to identify heart stroke disorders. The study incorporates various ML techniques alongside key factors such as hypertension, BMI, cardiac disease, mean glucose levels, tobacco use, previous stroke incidents, and patient age. A comparative analysis and review of diverse machine learning techniques employed to predict heart stroke occurrence has been presented in the paper. A performance analysis of Gradient Boosting Classifier (GBC), AdaBoost Classifier, Support Vector Classifier (SVC), Random Forest Classifier (RFC), and Logistic Regression Classifier (LR) has been performed and the results have been evaluated using the confusion matrix followed by key findings. © 2025 IEEE.

关键词： Logistic regression

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Deep learning and IoT-Enabled Framework for Accurate Classification and Monitoring of Alzheimer's Disease Based on EEG Signal Analysis 5

Deep Learning and IoT-Enabled Framework for Accurate Classif...

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5th IEEE international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Sathish, R. Muthukumar, R. Dhivya, K. Karthikkumar, S. Builders Engineering College Department of Electrical and Electronics Engineering Tamilnadu Tirupur India Erode Sengunthar Engineering College Department of Electrical and Electronics Engineering Tamilnadu Perundurai India Jai Shriram Engineering College Department of Electrical and Electronics Engineering Tamilnadu Tirupur India

ISBN: (纸本)9798350356236

Alzheimer's disease is a brain ailment that impairs thinking, memory, and behaviour. The efficacy of Brain- Computer Interface (BCI) systems must be enhanced to increase their prevalence in the biomedical sector. The biomedical sector is crucial for precise and efficient patient diagnostics using digital signal processing and wireless sensor network technologies. Alzheimer's disease (AD) is an irreversible brain disorder. Inadequate Alzheimer's disease diagnosis in the early stages, together with exact identification, may aid in halting the progression of the illness. Precise and effective detection and categorization of Alzheimer's disease enhance quality of life and increase life expectancy for patients. The advanced deep learning technology surpasses traditional machine learning methods in classifying complex configurations in high-dimensional composite data, particularly in digital signal processing. The deep learning application for the early detection and accurate classification of Alzheimer's disease might be suggested for a neuron-signal methodology in extensive data analysis. This research paper proposes an intelligent decision-making system (IDMS) for the diagnosis and categorization of Alzheimer's disease (AD) and the continuous monitoring of patients utilizing connected wearable devices. Methods for measuring neuronal synchrony are proposed to diagnose and characterize Alzheimer's disease. © 2025 IEEE.

关键词： Neurodegenerative diseases

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A Semantic Driven Framework for Energy Efficient Task Scheduling in Dynamic Cloud Environment 4

A Semantic Driven Framework for Energy Efficient Task Schedu...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Aarav Kannan, J. Amshavalli, R.S. Venkateswara Reddy, D. Varma, Vegesna Surya Vinay Kavitha, G. Srm Institute of Science and Technology Dept of Biomedical Engineering Chennai India Sathyabama Institute of Science and Technology Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331523923

The rapid expansion of cloud computing has led to increased energy consumption, raising concerns about its environmental impact. Despite advancements in scheduling algorithms, existing methods face critical challenges, including suboptimal energy efficiency, limited adaptability to dynamic cloud environments, and insufficient context-aware decision-making. Recent techniques, such as Meta Reinforcement learning, Genetic Algorithms, and Energy-Aware Scheduling, show potential but often struggle with high computational complexity, static resource allocation, or scalability issues. These shortcomings result in inefficient resource utilization, elevated energy usage, and difficulties in meeting Service Level Agreement (SLA) requirements. To address these challenges, this study introduces the Energy Efficient Semantic Scheduling Framework (ESSF), a novel approach combining semantic reasoning and energy profiling to optimize task scheduling in dynamic cloud environments. The framework integrates three key components: (1) Dynamic Energy Profiling (DEP) for real-time energy monitoring, (2) Semantic Resource Matching (SRM) utilizing knowledge graphs for context-aware allocation, and (3) Adaptive Workload Balancing (AWB) to dynamically redistribute tasks for improved resource utilization and energy efficiency. Experiments using the Google Cluster Trace Dataset demonstrate that ESSF reduces energy consumption by up to 25% compared to traditional methods while achieving 90% resource utilization and 97% SLA compliance. These results highlight the potential of the proposed framework to overcome existing limitations, offering a scalable, adaptive, and environmentally sustainable solution for task scheduling in cloud computing. © 2025 IEEE.

关键词： Cloud computing Dynamic Energy Profiling Energy Efficiency Google Cluster Trace Dataset Knowledge Graphs Semantic Scheduling

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Leadership teaching in Agile Software engineering: A Systematic Mapping 17

Leadership Teaching in Agile Software Engineering: A Systema...

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17th international conference on Computer Supported Education, CSEDU 2025

作者： Nascimento, Nicolas Sales, Afonso Chanin, Rafael PUCRS School of Technology RS Porto Alegre Brazil

ISBN: (纸本)9789897587467

The software industry is characterized by an environment of uncertainty, high volatility, and constant change. This context has shaped the industry, its components, and actors, generating methodologies capable of meeting both market expectations and software development requirements. Among these methodologies, agile has been the most widely adopted - it provides teams with an overarching set of practices to manage software development project requirements while maintaining flexibility to incorporate changes that the business environment presents. However, professionals equipped with the necessary skills to work in these Agile teams, often referred to as "soft skills", pose a challenge for universities to teach. From this set of skills, leadership, in particular, has recently garnered attention from the software engineering education community but remains an open research opportunity. In this context, this work aims at creating a body of knowledge regarding leadership teaching in agile software engineering. To achieve this goal, we conducted a systematic mapping. From a selection of 27 studies, our results provide indications that: (i) leadership in software engineering education is typically defined as part of teamwork, shared among team members, associated with Scrum, and applied to provide students with experience in group representation;and (ii) it is usually taught through Scrum, involving active learning methodologies (such as problem-based learning) and employing real projects either sourced from external partners or designed to solve real-world problems. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Software design

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Improvement of Image Classification Technique Using Transfer learning 5

Improvement of Image Classification Technique Using Transfer...

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5th IEEE international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Bhonsle, Devanand Uzma, Ruhi Prasad, Priyanka Bankar, Rushikesh Nayak, Manjushree Massey, Sampada Vishwas Rizvi, Tanu Shri Shanakaracharya Technical Campus Bhilai India Anjuman College of Engineering & Technology Nagpur India Shri Shanakaracharya Institute of Professional Management & Tech Bhilai India G. H. Raisoni Institute of Engineering and Technology Nagpur India Nist University Odisha Berhampur India Shri Shanakaracharya Technical Campus Bhilai India

ISBN: (纸本)9798350356236

Image classification is one of the fundamental task in the digital image processing and computer vision. It is very essential to get high accuracy in it which requires substantial computational resources and a large dataset for the training using deep neural networks. Transfer learning technique is one of the solution which mitigate various challenges by leveraging pre-trained models on large datasets. In this paper;the effectiveness of the transfer technique is exhibited for the purpose of enhancing the image classification. A comprehensive survey has been done of the recent advancement for the purpose to investigate the strategies of transfer learning technique including feature extraction, fine tuning and domain adaption. Experimental results exhibits the efficacy of the transfer learning in the improvement of classification accuracy with less computational complexity. This paper also discusses practical consideration, challenges and future research scope for the utilization of transfer learning for image classification applications. © 2025 IEEE.

关键词： Transfer learning

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Latency optimization for task offloading in Space-Terrestrial collaborative networks based on deep reinforcement learning

Latency optimization for task offloading in Space-Terrestria...

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2024 international conference on Smart Transportation and City engineering, STCE 2024

作者： Song, Chuanlong Zhao, Yuxuan He, Jian School of Communication and Information Engineering Shanghai University Shanghai China Shanghai Aerospace Electronic Technology Institute Key Laboratory of Intelligent Computing for Spatial Heterogeneous Network Shanghai China

ISBN: (纸本)9781510690585

Satellite networks, as a crucial component of space communication systems, effectively mitigate the limitations of terrestrial communication networks in edge computing, such as insufficient ground station coverage and susceptibility to natural disasters. Therefore, this paper introduces satellite networks into the context of terrestrial edge computing, constructing a space-terrestrial collaborative network edge computing framework and investigating the latency issues in task offloading. Due to factors such as long communication distances in space-terrestrial networks, limited onboard resources, and rapid relative positional changes between nodes, system latency may increase. This study models the problem as a Markov Decision Process (MDP) with the objective of minimizing system latency. A Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm is proposed for the space-terrestrial collaborative network edge computing scenario. Experimental results demonstrate that, compared to local task processing and the Deep Q-Network (DQN) algorithm, the proposed algorithm reduces the overall system overhead by an average of 28.12% and 13.29%, respectively, while also exhibiting better stability and convergence. © 2025 SPIE.

关键词： deep reinforcement learning edge computing satellite-ground collaborative network task offloading

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Approaches to Research-Based teaching and learning 27th

Approaches to Research-Based Teaching and Learning

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27th international conference on Interactive Collaborative learning, ICL 2024

作者： Restivo, Maria Teresa Zvacek, Susan M. LAETA University of Porto Porto3ES Portugal *** London United States

ISBN: (纸本)9783031835223

This article introduces the idea of research-based learning approaches and provides examples of such initiatives developed and implemented at the University of Porto (UPorto) and its Faculty of engineering (FEUP). This pedagogical approach, in which students are directly engaged in research, is described and the benefits and challenges of its implementation are discussed. Specific projects that have been undertaken at UPorto and FEUP using this model are also described, including multidisciplinary initiatives and young researcher events. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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Enhancing Milk Yield Forecasting in Dairy Farming Using an Interpretable Machine learning Framework 4

Enhancing Milk Yield Forecasting in Dairy Farming Using an I...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Srinithi, B. Sruthi Nirmala, S.R. Thangavel, Senthil Kumar Somasundaram, K. Ramasamy, M. Amrita School of Physical Sciences Coimbatore Department of Mathematics Amrita Vishwa Vidyapeetham Tamil Nadu India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India Icarkvk Virinjipuram TanilNadu Vellore India

ISBN: (纸本)9798331523923

This study employs machine learning techniques and explainable AI (XAI) to enhance milk yield predictions and optimizes dairy farming practices. Split k-means clustering, KNN, SVM combined with k-means, and regression techniques such as binomial, polynomial, and logistic regression are all used in this methodology. KNN in conjunction with SVM improves prediction accuracy even further. Explainable AI techniques, such as SHAP, provide clarity by emphasizing feature significance, while diverse representations improve data scrutiny. These methods offer practical insights into improving dairy farming operations, ensuring efficient resource utilization, and increasing milk output. © 2025 IEEE.

关键词： Adversarial machine learning

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