Electroencephalogram (EEG) analysis plays a critical role in diagnosing various neurological conditions by detecting abnormal brain activities. However, the complex nature of EEG signals poses challenges for tradition...
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Integrating physiological signals such as electroencephalogram (EEG), with other data such as interview audio, may offer valuable multimodal insights into psychological states or neurological disorders. Recent advance...
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ISBN:
(纸本)9798400710582
Integrating physiological signals such as electroencephalogram (EEG), with other data such as interview audio, may offer valuable multimodal insights into psychological states or neurological disorders. Recent advancements with Large Language Models (LLMs) position them as prospective "health agents" for mental health assessment. However, current research predominantly focus on single data modalities, presenting an opportunity to advance understanding through multimodal data. Our study aims to advance this approach by investigating multimodal data using LLMs for mental health assessment, specifically through zero-shot and few-shot prompting. Three datasets are adopted for depression and emotion classifications incorporating EEG, facial expressions, and audio (text). The results indicate that multimodal information confers substantial advantages over single modality approaches in mental health assessment. Notably, integrating EEG alongside commonly used LLM modalities such as audio and images demonstrates promising potential. Moreover, our findings reveal that 1-shot learning offers greater benefits compared to zero-shot learning methods.
This paper describes a system that aims to provide course instructors with the capability to create and conduct formative and summative peer code review exercises for their students. It also aims to improve student en...
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One of job recruiters' biggest challenges is selecting a suitable resume from the pool of resumes. For a single role, thousands of candidates send their resumes. Manually selecting the resume from a large number o...
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This research explores the augmentation of Agricultural Internet of Things (IoT) systems through the integration of advanced predictive analytics and reinforcement learning models. A novel algorithm, termed "Crop...
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Unsupervised vision clustering, a cornerstone in computer vision, has been studied for decades, yielding significant outcomes across numerous vision tasks. However, these algorithms involve substantial computational d...
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ISBN:
(纸本)9798331541378
Unsupervised vision clustering, a cornerstone in computer vision, has been studied for decades, yielding significant outcomes across numerous vision tasks. However, these algorithms involve substantial computational demands when confronted with vast amounts of unlabeled data. Conversely, quantum computing holds promise in expediting unsupervised algorithms when handling large-scale databases. In this study, we introduce QClusformer, a pioneering Transformer-based framework leveraging quantum machines to tackle unsupervised vision clustering challenges. Specifically, we design the Transformer architecture, including the self-attention module and transformer blocks, from a quantum perspective to enable execution on quantum hardware. In addition, we present QClusformer, a variant based on the Transformer architecture, tailored for unsupervised vision clustering tasks. By integrating these elements into an end-to-end framework, QClusformer consistently outperforms previous methods running on classical computers. Empirical evaluations across diverse benchmarks, including MS-Celeb-1M and DeepFashion, underscore the superior performance of QClusformer compared to state-of-the-art methods.
Lifestyle diseases, including cardiovascular conditions, obesity, type 2 diabetes, and hypertension, are increasing global health concerns, particularly in developing countries. These non-communicable diseases are oft...
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作者：
Bharti, VijayGoel, Rohini
Department Of Computer Science And Engineering Ambala Mullana133203 India
The trusted nature of imagery is more easily manipulated in this tech-driven era through copy-move forgery (CMF), when parts of an image are copied and reinserted into the identical frame to trick users. The diversity...
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Brain tumors represent a significant and often life-threatening medical challenge, demanding accurate and timely diagnosis for effective treatment planning. In this study, we will be comparing various deep learning ar...
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Intrusion Detection Systems (IDS) play a pivotal role in safeguarding the integrity and performance of an organization. Throughout recent years, various approaches have been devised and put into practice to fortify th...
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