Human-in-the-Loop (HITL) Machine Learning with Ensemble Learning uses the best of both automated algorithms and human input in making predictions and decision making when it comes to Machine Learning (ML). In HITL fra...
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ISBN:
(数字)9798350356236
ISBN:
(纸本)9798350356243
Human-in-the-Loop (HITL) Machine Learning with Ensemble Learning uses the best of both automated algorithms and human input in making predictions and decision making when it comes to Machine Learning (ML). In HITL frameworks they include human inputs in model training and validation, allowing the resultant models to be built based on actual needs rather than white algorithms, which may be insensitive to some details otherwise. It is more relevant in such areas or applications that involve higher risk such as credit risk applications in today's financial world where predictions form the basis for major loans' approval. Although previous studies that employed fully automated ML models have some shortcomings that include being prone to giving biased predictions, being less interpretable and scalability issues and failure to handle edge cases hence reducing reliability and trustworthiness. To overcome these problems, this study introduces a new approach of combining the HITL with the XGBoost, which is one of the most potent ensemble learning algorithms to develop a balanced model of loan default prediction. Through the analysis of loan data from Lending Club, this method has attained an accuracy of 99.4% along with a high precision and a high recall, thus pointing to well-rounded performance of the model. Expert knowledge in the form of the HITL loop improves the regularization and optimization of the XGBoost model leading to a better, efficient, reliable, and more interpretable model for credit scoring. It was in python the model was developed taking advantage of the powerful ml and data analysis libraries which make it easy to develop, scalable and reusable for real life organizations especially in the financial arena where precision and accuracy are paramount. This approach lays some basis for subsequent innovations in the ICT applications in HITL ML systems in different fields.
Inspired by the dynamic coupling of moto-neurons and physical elasticity in animals, this work explores the possibility of generating locomotion gaits by utilizing physical oscillations in a soft snake by means of a l...
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Multimodal Large Language Models (MLLMs) have demonstrated proficiency in processing diverse modalities, including text, images, and audio. These models leverage extensive pre-existing knowledge, enabling them to addr...
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Sentiment analysis, or more specifically, the integration of IoMT into healthcare systems, requires frameworks that must adapt at runtime with high precision. Most existing methods have several limitations of either l...
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Recommendation systems (RSs) have become indispensable features in nearly all web applications. Sifting through data and alleviating information overload, these systems offer more streamlined and personalized recommen...
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There are many approaches in mobile data ecosystem that inspect network traffic generated by applications running on user’s device to detect personal data exfiltration from the user’s device. State-of-the-art method...
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As a dynamic network that links important elements of the commerce ecosystem, including buyers, sellers, products, and transactions, the Commerce Graph is a ground-breaking development in digital commerce. By using ad...
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As a dynamic network that links important elements of the commerce ecosystem, including buyers, sellers, products, and transactions, the Commerce Graph is a ground-breaking development in digital commerce. By using advanced centrality metrics, such as degree, betweenness, and closeness, to identify key consumers and products, this study exemplifies its capacity for transformation. The findings show that the most important roles in the network are played by customers with higher centrality ratings (such as IDs 66, 91, and 47) and well-known products (such as IDs 102, 104, and 100). With the top buyers in 2018 displaying noteworthy engagement tendencies, temporal research reveals changing customer behaviours. Additionally, buyer and product clusters are found using community detection employing the Louvain algorithm, allowing segmentation tactics to maximise marketing and consumer engagement. The study offers practical insights that may be used to improve corporate operations and identify targets for customized promotions. This study makes the Commerce Graph an essential tool for tackling issues like scalability and incorporating sophisticated algorithms.
Reconfigurable intelligent surfaces (RISs) have emerged as a promising auxiliary technology for radio frequency imaging. However, existing works face challenges of faint and intricate back-scattered waves and the rest...
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An (n, R)-covering sequence is a cyclic sequence whose consecutive n-tuples form a code of length n and covering radius R. Using several construction methods improvements of the upper bounds on the length of such sequ...
User Intent Detection is one of the primary tasks in dialogue systems and chatbots, playing a key role in improving human-machine interaction. With the increasing use of chatbots in the Persian language, there is a gr...
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ISBN:
(数字)9798331508913
ISBN:
(纸本)9798331508920
User Intent Detection is one of the primary tasks in dialogue systems and chatbots, playing a key role in improving human-machine interaction. With the increasing use of chatbots in the Persian language, there is a growing need to develop efficient methods for detecting user intent in this language. This review article provides a comprehensive overview of the methods, challenges, and recent advancements in the field of user intent detection in Persian text-based chatbots. The review discusses deep learning-based methods, pre-trained language models, and language-specific challenges. Additionally, it analyzes existing datasets and data augmentation techniques to improve model performance. The findings of this review indicate that, despite recent advancements, challenges such as the scarcity of labeled data, structural differences between Persian and other languages, and the need for multilingual models persist. Finally, future research directions are proposed to enhance user intent detection in Persian chatbots.
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