Everyday, many individuals face online trolling and receive hate on different social media platforms like Twitter, Instagram to name a few. Often these comments involving racial abuse, hate based on religion, caste ar...
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ISBN:
(纸本)9798350339369
Everyday, many individuals face online trolling and receive hate on different social media platforms like Twitter, Instagram to name a few. Often these comments involving racial abuse, hate based on religion, caste are made by anonymous people over the internet, and it is quite a task to keep these comments under control. So, the objective was to develop a Machine Learning Model to help identify these comments. A Deep Learning Model (a sequential model) was made and it was trained to identify and classify a comment based on whether it is an apt comment or not. LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) that is particularly well-suited for modeling sequential data, such as text. LSTMs are capable of modeling long-term dependencies in sequential data. In the case of text classification, this means that LSTMs can take into account the context of a word or phrase within a sentence, paragraph, or even an entire document. LSTMs can learn to selectively forget or remember information from the past, which is useful for filtering out noise or irrelevant information in text. LSTMs are well-established in the field of natural language processing (NLP) and have been shown to be effective for various NLP tasks, including sentiment analysis and text classification. Binary cross-entropy is a commonly used loss function in deep learning models for binary classification problems, such as predicting whether a comment is toxic or not. Binary cross-entropy is designed to optimize the model's predictions based on the binary nature of the classification task. It penalizes the model for assigning a low probability to the correct class and rewards it for assigning a high probability to the correct class. The loss function is differentiable, which allows gradient-based optimization methods to be used during training to minimize the loss and improve the model's performance. Binary cross-entropy is a well-established loss function that has been extensively used
While drilling, in order to realize the early monitoring of overflow,it is necessary to measure the liquid level depth in the wellbore in real time. At present, echo ranging is the most commonly used liquid level moni...
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Modern visualization methods are used to convey information about an object or process and as a tool for search and decision-making process. data and signals, in analog and digital form, are only valuable if they are ...
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The work revealed that the destruction process occurs in three stages. Each stage is characterized by its own kinetic model. The values of the pre-exponential factor and the activation energy of the reaction rate cons...
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ISBN:
(数字)9798331532178
ISBN:
(纸本)9798331532185
The work revealed that the destruction process occurs in three stages. Each stage is characterized by its own kinetic model. The values of the pre-exponential factor and the activation energy of the reaction rate constant were obtained. The calculation according to this method with the found values of the reaction rate constants showed a fairly good match with the experimental data.
This research enhanced the Discrete Logarithm Problem-Based (DLP-based) Algorithm, enabling it to utilize two or more security keys in the encryption process. The enhancement enabled the Algorithm to inject multiple h...
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As the security issues of Internet of Things (IoTs) are rapidly evolving, machine learning techniques are increasingly adopted for detecting and preventing cyber threats. Recent machine learning based approaches (e.g....
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Early detection and characterization of anomalous events during drilling operations are critical to avoid costly downtime and prevent hazardous events, such as a stuck pipe or a well control event. A key aspect of rea...
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ISBN:
(纸本)9781613999929
Early detection and characterization of anomalous events during drilling operations are critical to avoid costly downtime and prevent hazardous events, such as a stuck pipe or a well control event. A key aspect of real-time drilling dataanalysis is the capability to make precise predictions of specific drilling parameters based on past time series information. The ideal models should be able to deal with multivariate time series and perform multi-step predictions. The recurrent neural network with a long short-term memory (LSTM) architecture is capable of the task, however, given that drilling is a long process with high data sampling frequency, LSTMs may face challenges with ultra-long-term memory. The transformer-based deep learning model has demonstrated its superior ability in natural language processing and time series analysis. The self-attention mechanism enables it to capture extremely long-term memory. In this paper, transformer-based deep learning models have been developed and applied to real-time drilling data prediction. It comprises an encoder and decoder module, along with a multi-head attention module. The model takes in multivariate real-time drilling data as input and predicts a univariate parameter in advance for multiple time steps. The proposed model is applied to the Volve field data to predict real-time drilling parameters such as mud pit volume, surface torque, and standpipe pressure. The predicted results are observed and evaluated. The predictions of the proposed models are in good agreement with the ground truth data. Four Transformer-based predictive models demonstrate their applicability to forecast real-time drilling data of different lengths. Transformer models utilizing non-stationary attention exhibit superior prediction accuracy in the context of drilling data prediction. This study provides guidance on how to implement and apply transformer-based deep learning models applied to drilling dataanalysis tasks, with a specific focus o
The attention mechanism within the transformer architecture enables the model to weigh and combine tokens based on their relevance to the query. While self-attention has enjoyed major success, it notably treats all qu...
The use of face recognition technology can greatly improve the effectiveness of the management of college students’ apartments in view of the current situation that there are many security risks in school apartments,...
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A Digital Twin (Applied Twin™) is a computational model that represents a physical asset such as a process chamber, evolving over time to reflect its structure, behavior, and context. This model treats the asset-twin ...
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ISBN:
(数字)9798331531850
ISBN:
(纸本)9798331531867
A Digital Twin (Applied Twin™) is a computational model that represents a physical asset such as a process chamber, evolving over time to reflect its structure, behavior, and context. This model treats the asset-twin system as a set of coupled dynamical systems that evolve over time, interacting through observed data and control inputs. [1] They are an evolution of modeling & simulation. The Digital Twin (DT) can be useful in integration, testing, monitoring, and maintenance. Eventually it can be utilized as an early and ad hoc fast learning tool for systems and processes. In semiconductor manufacturing, DTs have found their way in their early rudimentary form as control or predictive applications such as for production control applications like planning, scheduling, and dispatching and for equipment and processcontrol applications like, Run-to-Run (Applied SmartFactory™ Run-2-Run) (R2R), Virtual Metrology, and predictive maintenance. Through this paper we explore the idea of idea of engineering DT and R2R algorithms with intersecting system manipulated variables or inputs between the two systems.
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