Most artificial intelligence (AI) applications are designed under the model-centric AI (MCAI) approach, where data scientists aim to optimize the machine learning (ML) models starting with fixed, preprocessed data. Ho...
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In this work, we address the challenge of efficiently modeling dynamical systems in process engineering. We use reduced-order model learning, specifically operator inference - a non-intrusive, data-driven method for u...
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The aim of the study is to evaluate the methods of autonomous collection and use of local contexts for selecting an answer option by an intelligent system in the dialog interaction process. The following research meth...
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ISBN:
(数字)9798331531836
ISBN:
(纸本)9798331531843
The aim of the study is to evaluate the methods of autonomous collection and use of local contexts for selecting an answer option by an intelligent system in the dialog interaction process. The following research methods were used: analysis of technical documentation, scientific papers, conference materials and instructional manuals. The current methods of collecting local contexts used in the most successful projects are analyzed. The methods of coding text words for processing by software are analyzed. The methods of measuring the quality of data collected in this way are analyzed. The result of the work is the presentation of the studied methods of collecting, storing and evaluating local contexts. A method for modeling subcategory contexts by a subject area is proposed in order to improve the quality of the decision-making system. A new method of graph modeling of logical connections of natural language data is proposed. A comparison of the effectiveness of various approaches to modeling local contexts is carried out and the corresponding conclusions are made.
A DW is needed to efficiently and continuously prepare data for practical real-time dataanalysis and forecasting with machine learning (ML) algorithms. This paper discusses extraction, transformation, and loading pro...
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ISBN:
(数字)9798331508180
ISBN:
(纸本)9798331508197
A DW is needed to efficiently and continuously prepare data for practical real-time dataanalysis and forecasting with machine learning (ML) algorithms. This paper discusses extraction, transformation, and loading process (ETL) automation and the critical subsystems integration for algorithmic trading ML modelling and stock market forecasting. Additionally, we discuss a new API designed for direct access to the DW. It opens vast opportunities for performance and dataprocessing time to become critical nonfunctional requirements.
The Wafer Acceptance Test (WAT) is a significant quality control measurement in the semiconductor industry. However, because the WAT process can be time-consuming and expensive, sampling test is commonly employed duri...
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ISBN:
(数字)9783982674100
ISBN:
(纸本)9798331534646
The Wafer Acceptance Test (WAT) is a significant quality control measurement in the semiconductor industry. However, because the WAT process can be time-consuming and expensive, sampling test is commonly employed during production. This makes root cause tracing impossible when abnormal products have not been tested. Therefore, in our study, we focus on establishing a reliable method to estimate WAT results for non tested shots, including both intra and inter-wafer prediction. Notably, we are the first to combine the use of Chip Probing data with WAT to improve the predictions. Our proposed method first extracts valuable features from Chip Probing test results by using the Automated Machine Learning technique. We then employ Gaussian process Regression to capture the spatiotemporal correlation. Finally, we adopted the linear regression model to ensemble two components and proposed a SMART-WAT model to effectively estimate the wafer acceptance test data. Our method has been tested on a real-world dataset from the semiconductor manufacturing industry. The prediction results of four key WAT parameters indicate that our proposed model outperforms the state-of-the-art methods in both intra and inter-wafer prediction.
This paper explores the measurement and analysis of extrusion forces in the Fused Deposition modeling (FDM) process, utilizing a custom-built 3D printer designed for high-performance additive manufacturing. A load cel...
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Smart Health Prediction Using Machine Learning' is a state-of-the-art, advanced system that will use high-power predictive modeling to predict diseases based on the entered symptoms. The proposed system will go th...
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Floods are a serious threat to human lives and property, with causes ranging from heavy rainfall to human-induced land-use changes and resulting hydrological consequences. In particular, the northern regions of India,...
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This research focuses on the design and optimization of antennas tailored for C-band frequency applications, spanning the microwave range of 4GHz to 8GHz. Two distinct antennas are meticulously crafted to cater to spe...
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This study presents a comparative analysis between a mixed integer linear programming (MILP) model and a single-agent artificial intelligence (AI) model trained with deep reinforcement learning (DRL) to optimize procu...
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