Credit cards became one of the most popular payment methods as technology advanced and e-commerce services expanded, resulting in an increase in the volume of banking transactions. Furthermore, the significant increas...
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Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language *** modeling,crucial for AI development,has evolved from statistical to neural models over the last two ***,trans...
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Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language *** modeling,crucial for AI development,has evolved from statistical to neural models over the last two ***,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training *** the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models *** advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal *** survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language ***,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI ***,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.
Graph Neural Networks (GNNs) have emerged as a widely used and effective method across various domains for learning from graph data. Despite the abundance of GNN variants, many struggle with effectively propagating me...
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作者：
Mathur, AshwiniBabu, S. Anantha
Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India
An Internet of Things (IoT) appears to be an innovative technology with great potential for widespread development. There has been a rise in data security issues in recent years as a consequence of various technologic...
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A vital part of today's transportation infrastructure is the railway system that facilitates the massive movement of people and products. With so many people and commodities being transported, the rail tracks coul...
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Forecasting the price of bitcoins is significant in contemporary research, given the fact that the digital currency is relatively unpredictable and highly integrated in global securities markets. This paper compares t...
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作者：
Vivek, V.Tr, Mahesh
Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India
The computer system that is used to take attendance online is going to be upgraded as part of this project. This attendance tracking system is able to hold the technology known as facial recognition, which is a useful...
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Software cost estimation is a crucial aspect of software project management,significantly impacting productivity and *** research investigates the impact of various feature selection techniques on software cost estima...
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Software cost estimation is a crucial aspect of software project management,significantly impacting productivity and *** research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 *** applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model *** findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation *** is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.
Any plant's ability to grow disease-free is crucial for both the environment and human existence. Nevertheless, various diseases, viruses, and fungi affect the plant and highly influence the yield quality and prod...
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In recent years, there has been a significant increase in attention toward emotion detection in text analysis, driven by its broad applications across marketing, political science, psychology, human-computer interacti...
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ISBN:
(纸本)9798350378511
In recent years, there has been a significant increase in attention toward emotion detection in text analysis, driven by its broad applications across marketing, political science, psychology, human-computer interaction, and artificial intelligence. This growing interest is primarily due to the critical role of textual expression as a repository of human emotions and sentiments. The development of sophisticated natural language processing (NLP) techniques has emphasized the importance of exploring emotion detection and recognition within textual data. By utilizing a wide range of sources, including social media content, microblogs, news articles, and customer feedback, text mining aims to reveal the underlying emotional currents within the text. However, existing models often struggle to capture the complicated emotional nuances woven into words. Addressing this challenge, the innovative semantic emotion neural network (SENN) architecture has been introduced. The SENN model marks a significant advancement, featuring two synergistic sub-networks: a bidirectional long short-term memory (BiLSTM) network that extracts contextual information and a convolutional neural network (CNN) that analyzes and extracts emotional features, highlighting the text's intrinsic emotional connections. The SENN model's performance has been thoroughly evaluated on widely used real-world datasets, benchmarked against Ekman's six fundamental emotions. Results demonstrated its superiority, showing that the SENN model excels in emotion recognition accuracy and quality in conjunction with additional techniques. It also holds potential for enhancement by incorporating more comprehensive emotional word embedding, suggesting a promising future for text-based emotion analysis. The proposed paper presents goals for detecting sentiment in text data and introduces a novel architecture that effectively captures the complexity of emotional nuances. We create an abstract model and compare three types of m
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