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A Coordinate Measuring Machine (CMM) is a precision measuring metrology machine used in manufacturing and quality control processes to precisely measure the geometric characteristics of objects. It operates on the principle of coordinate geometry, employing a probe to collect data points from the surface of an object to determine the dimensions, shapes, and positions of features, such as holes, slots, and surfaces. CMMs are essential tools for ensuring that manufactured parts meet design specifications and quality standards.

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8th International Conference on Soft Computing, Mathematics and Control (SMC 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications impacts and challenges of Soft Computing, Mathematics and Control. The conference documents practical and theoretical results which make a fundamental contribution for the development of Soft Computing, Mathematics and Control. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

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Exploring AI's Impact: Key Features in Due Diligence In the realm of due diligence, AI emerges as a game-changer, revolutionizing traditional methods with its advanced features. AI-powered algorithms excel in data analysis, swiftly sifting through vast amounts of information for crucial insights. Automation streamlines document review processes, ensuring accuracy and efficiency. Moreover, AI enables predictive analytics, forecasting potential risks and opportunities with precision. With machine learning capabilities, AI continuously improves its performance, adapting to evolving trends and patterns. By integrating AI into due diligence practices, businesses gain a competitive edge, maximizing efficiency and making informed decisions swiftly. AI in due diligence is not just a tool; it's a transformational force driving businesses into the future. https://www.leewayhertz.com/ai-in-due-diligence/

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This presentation, crafted with expertise from the oil and gas industry, details the imperative techniques and tools used in the process of incident investigation. The presentation highlights the structured methods involving interviews with engaged parties, the importance of gathering documentary evidence, employing structured forms and checklists, the value of reviewing surveillance footage, the power of modern technology, and synergizing with subject-matter experts. It is designed for industry professionals keen on enhancing safety practices, regulatory compliance, and preventive measures within their operational environment. Valuable for health & safety officers, compliance managers, and operation leaders, it prioritises continuous learning and adherence to best practices to ensure workplace safety and operational excellence.

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This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.

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