This paper comprehensively analyses recent advances in machinelearning methods for early Alzheimer's disease (AD) diagnosis. It underscores the transformative impact of machinelearning in healthcare, improving p...
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Various types of machinelearning methods are being used to predict and estimate power stability in smart grids. These include both supervised and unsupervised machinelearning methods. This paper proposes the use of ...
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ISBN:
(纸本)9798350365924;9798350365917
Various types of machinelearning methods are being used to predict and estimate power stability in smart grids. These include both supervised and unsupervised machinelearning methods. This paper proposes the use of a semi-supervised learning method using a graph model for proactive learning in smart grids. The proposed method starts by labeling the unlabeled bus voltage data using information from the partially labeled bus voltage data. Then, it evaluates the score of the labeling and predicts the system stability by using correlation matrices. The method is applied for five power system test cases including the IEEE 14 bus, 30 bus, 39 bus, 57 bus, and 118 bus systems. The results confirm a high degree of correlation between the predicted results and the actual values of the terminal voltages of all the nodes in the experimental systems. The method can be used to predict the effects of disturbances in smart grids and related systems of systems.
The crop recommendation systems in agriculture frequently depend on general guidelines or personal experience, with little regard for important soil factors such as pH and nutrient concentrations. It also results in p...
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This position paper takes a broad look at Physics-Enhanced machinelearning (PEML) - also known as Scientific machinelearning - with particular focus to those PEML strategies developed to tackle dynamical systems'...
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machinelearning has enabled innovative usage in numerous fields. These systems are vulnerable to hostile assaults. Small intentional alterations to misclassify data constitute a maj or security risk. We examine three...
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Parkinson's Disease (PD) is the degeneration of the nervous system. Patient suffering with PD is going through many issues and among the population most of them are elderly peoples. Detection of this disease at it...
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The rapid development of Industry 4.0 technologies has brought predictive maintenance into focus, particularly for small and medium-sized enterprises (SMEs) where cost and complexity are major barriers. In this paper,...
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ISBN:
(纸本)9798350371000;9798350370997
The rapid development of Industry 4.0 technologies has brought predictive maintenance into focus, particularly for small and medium-sized enterprises (SMEs) where cost and complexity are major barriers. In this paper, we present an innovative approach to vibration analysis, a key component for fault detection in mechanical systems and the creation of digital twins. Utilizing MatLab, we generated synthetic data points to simulate various vibration scenarios. These synthetic data points served as the training set for our machinelearning model. The trained model was then integrated with a lowcost, Bluetooth-enabled accelerometer for real-time monitoring. Our system successfully identified fault conditions, specifically lump mass irregularities, through real-time sensor data. Our findings show promising capabilities for offering a cost-effective and straightforward solution for predictive maintenance. This research not only advances the field of vibration analysis but also opens doors for SMEs to embrace the benefits of digital twin technologies.
The proceedings contain 15 papers presendted at a virtual meeting. The special focus in this conference is on Software Architecture Erosion and Architectural Consistency. The topics include: Interactive Elicitation of...
ISBN:
(纸本)9783031151156
The proceedings contain 15 papers presendted at a virtual meeting. The special focus in this conference is on Software Architecture Erosion and Architectural Consistency. The topics include: Interactive Elicitation of Resilience Scenarios Based on Hazard Analysis Techniques;Towards an Extensible Approach for Generative Microservice Development and Deployment Using LEMMA;applying Knowledge-Driven Architecture Composition with Gabble;architectural Optimization for Confidentiality Under Structural Uncertainty;foundations and Research Agenda for Simulation of Smart Ecosystems Architectures;Blended Graphical and Textual Modelling of UML-RT State-machines: An Industrial Experience;toward Awareness Creation of Common Challenges Women are Facing in Academia: A Study from a German Perspective;mapping Source Code to Modular Architectures Using Keywords;hierarchical Code-to-Architecture Mapping;Building the MSR Tool Kaiaulu: Design Principles and Experiences;self-adaptive machinelearningsystems: Research Challenges and Opportunities;behavioral Maps: Identifying Architectural Smells in Self-adaptive systems at Runtime;an Architectural Approach for Enabling and Developing Cooperative Behaviour in Diverse Autonomous Robots.
This study examines the ways in which learning Management systems (LMS) have become an essential component of contemporary education, influencing both teacher effectiveness and student learning. It looks at the origin...
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engineering AI Software systems is starting to evolve from the pure development of machinelearning (ML) models to a more structured discipline that treats ML components as part of much larger software systems. As suc...
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engineering AI Software systems is starting to evolve from the pure development of machinelearning (ML) models to a more structured discipline that treats ML components as part of much larger software systems. As such, more structured principles are required for their development, such as established design principles, established development models, and safeguards for deployed ML models. This column focuses on papers presented at the Third internationalconference on AI engineering-Software engineering for AI (CAIN 2024). The selected papers reflect the current development of the field of AI systemsengineering and AI software development, taking it to the next level of maturity. Feedback or suggestions are welcome. In addition, if you try or adopt any of the practices included in the column, please send us and the authors of the paper(s) a note about your experiences.
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