Generalized Zero-Shot learning is a world-class challenge. in real life, many scenes have been used and have broad prospects for development. GZSL's purpose is to identify new classes by transferring semantic know...
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this work introduced an AI-based simulation method as an innovative solution to address the limitations of conventional numerical reservoir simulation methods. the primary motivations behind this research were the tim...
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Landslides are a frequent and widespread geological disaster. Because a large number of deadly landslide disasters are newly added every year, efficient landslide identification is of great significance in application...
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Next generation firewall is taking major part to secure network environment in the industry. this device will monitor all the traffic which is coming inside the network or going outside of the network. With all these ...
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Research on the dynamic modeling algorithm of college students' learning motivation factors based on SPSS and artificial intelligence testing machine is conducted in this paper. In the designed statistical model, ...
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ISBN:
(数字)9781665408370
ISBN:
(纸本)9781665408387;9781665408370
Research on the dynamic modeling algorithm of college students' learning motivation factors based on SPSS and artificial intelligence testing machine is conducted in this paper. In the designed statistical model, according to the results of the two-sample ANOVA, that is, whether the population variances of the two samples are equal, the t-test of the assumptions of equal variance and heteroscedasticity is selected to infer whether the population means of the two samples are equal. It reflects the variation degree of another variable after controlling the value of one variable. When both variables are normal continuous variables and there is a linear relationship between them on the scatter diagram, it can be considered that there is a linear correlation trend between them. Withthis designed novel model, the SPSS and artificial intelligence testing machine is designed. through the various testing, the general performance is validated.
the use of multilingual models for natural language processing is becoming increasingly popular in industrial and business applications, particularly in multilingual societies. In this study, we investigate the transf...
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the proceedings contain 19 papers. the topics discussed include: towards face representation learning conditioned on the soft biometrics;a semantic segmentation approach for road defect detection and quantification;de...
ISBN:
(纸本)9781450395670
the proceedings contain 19 papers. the topics discussed include: towards face representation learning conditioned on the soft biometrics;a semantic segmentation approach for road defect detection and quantification;depth and thermal images in face detection â€" a detailed comparison between image modalities;the study of emotional brain to detect emotions using brain EEG signals and improving accuracy of emotion detection system using feature selection techniques;transfer learning based precise pose estimation with insufficient data;analysis and example implementation of data visualization technology;simultaneous integration of multimodal interfaces for generating structured and reliable robotic task configurations;optimizing train-test data for person re-identification in real-world applications;and development of a powerful facial recognition system through an API using ESP32-Cam and Amazon Rekognition service as tools offered by industry 5.0.
Bacteria are implicated in a lot of biological and chemical activities, some of which are dangerous and others beneficial. Bacterial samples go through several stages before identification. Some of these stages are do...
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the requirements elicitation phase in the software development life cycle (SDLC) is both critical and challenging, especially in the context of big data and rapid technological advancement. Traditional approaches like...
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ISBN:
(数字)9798350355925
ISBN:
(纸本)9798350355932
the requirements elicitation phase in the software development life cycle (SDLC) is both critical and challenging, especially in the context of big data and rapid technological advancement. Traditional approaches like workshops and proto-typing, while useful, often struggle to keep pace withthe massive data volumes and rapidly changing user demands characteristic of modern technology. this paper introduces a data-driven approach that utilizes deep learning (DL) and natural language processing (NLP) to enhance the requirements elicitation process by extracting requirements and classifying them into functional and non-functional categories. Our research involves a deep neural network (DNN) trained on a large dataset of transcriptions from client/user stories. this DNN can identify whether a specific text represents a functional requirement, a non-functional requirement, or neither. Our approach shows a marked improvement over previous methods, with a 33% increase in accuracy and an 18% increase in the F1 score. these results indicate the capability for deep learning techniques to play a vital role in elicitation.
this paper presents an advanced method for condition monitoring and fault detection in induction motors using a Multiclass Extreme learningmachine (ELM) classification system and enhanced for feature visualization by...
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