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检索条件"丛书名=Communications in Computer and Information Science"

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ASTGCN for Traffic Flow Prediction Based on Weather Influence 5th

ASTGCN for Traffic Flow Prediction Based on Weather Influenc...

引用

5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Zhou, Jianlin Song, Minyuan Zheng, Kaiyuan Ma, Lujuan Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China

ISBN: (纸本)9789819770069;9789819770076

The growing complexity of urban traffic networks necessitates advancements in traffic flow prediction. Traditional methods frequently fail to deliver accurate predictions, primarily due to their oversimplified nature and the omission of key factors, notably weather conditions. To overcome these deficiencies, this paper proposes the Weather-Influenced Attention-Based Spatio-Temporal Graph Convolutional Network (WI-ASTGCN), an advanced refinement of the ASTGCN. This model judiciously incorporates weather weighting factors, chosen for their strong correlation with traffic flow, such as atmospheric temperature, relative humidity, and wind speed. It adeptly captures the complex spatiotemporal dynamics and adapts in real-time to weather changes, enhancing prediction accuracy. The experimental results demonstrate that the model has better predictive performance compared to the six existing baseline methods. Compared to the ASTGCN model, the overall performance of the model in terms of reduction of prediction error is improved by about 8%.

关键词： weather weighted factor graph convolutional network traffic flow prediction

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Priority-Based Weighted Constrained Crowd Judgement Analysis with Quantum Genetic Algorithm 4th

Priority-Based Weighted Constrained Crowd Judgement Analysis...

引用

4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Mandal, Suraj Chatterjee, Sujoy Mukhopadhyay, Anirban Univ Kalyani Kalyani W Bengal India Univ Estadual Campinas Sao Paulo Brazil Indian Inst Technol Kanpur Kanpur Uttar Pradesh India

ISBN: (纸本)9783031837920;9783031837937

Crowdsourcing has already obtained a lot of attention from researchers due to the enormous power of solving complex problems in less time and at a minimal cost. Most of the research considers finding aggregated judgment from multiple diverse opinions of workers, and several algorithms such as majority-voting, weighted majority voting, and Expectation-Maximization (EM) algorithms are widely accepted. Recently, a relatively unexplored area of judgement analysis algorithm was introduced in the literature, and each crowd worker provides their opinions that comprise multiple components for a single question, and there is a constraint for each pair of components. However, in state-of-the-art approaches, the weight of the opinions is not considered. In this work, a new variant of the problem is introduced, while the weights between the components are considered. More importantly, the weights are not predefined, rather they are collected from the crowd workers. Thus, it can help us to explore many other solutions based on the users' preferences which is very difficult to obtain from decision-makers if the crowdsourcing technique is not employed. Quantum computing has been a new approach to computer science that explores the idea of parallelism in computing. In this work, we have introduced a quantum genetic algorithm that explores solving the multiple objective-based weighted constrained crowd judgment analysis. In addition to that, we have introduced a new variant of constrained crowd judgment analysis that not only explores the solution to the multiple opinion-based solutions but also makes a better interpretation of the solutions by including textual information and additional weight parameters. The algorithm is applied to a real-life crowdsourced dataset obtained from the campus of the University of Kalyani. The experimental results demonstrate the applicability of the proposed method while providing a better solution compared to the traditional method.

关键词： Crowdsourcing Constraint Judgment Analysis Weighted Judgment

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Balancing Class Distribution in Microarray Analysis Using GenAI 4th

Balancing Class Distribution in Microarray Analysis Using Ge...

引用

4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Anurekha, G. Amutha, S. Nivethika, K. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9783031821554;9783031821561

Microarrays are sophisticated datasets that have a substantial number of features and a limited number of samples. This can lead to a class imbalance problem. Balancing class distribution is an important research domain. In this paper, a novel variational auto encoder model is proposed for class imbalance mitigation. The proposed model employs an adversarial loss function to generate simulated data. The proposed model is evaluated using the autism gene expression dataset. The obtained experimental results prove that the data generated from the proposed model have obtained noteworthy results compared with the original dataset by employing three different classifiers.

关键词： Class imbalance Deep learning Microarray Simulated data Variational autoencoder

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Ensuring Advanced ATM Security: A Formal Verification Approach with Event-B 4th

Ensuring Advanced ATM Security: A Formal Verification Approa...

引用

4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Karmakar, Rahul Mandal, Aditi Univ Burdwan Bardhaman West Bengal India

ISBN: (纸本)9783031837821;9783031837838

The research examines the use of Event-B, a formal modeling language, to design and verify a security-enhanced ATM system that counteracts sophisticated theft and fraud. Event-B's rigorous framework allows for precise specification and refinement of system requirements, embedding security protocols such as biometric authentication, AI-driven monitoring, and two-factor authentication. These features enhance the reliability and safety of ATM operations, while formal methods in Event-B help identify and prevent unauthorized access and tampering. Advanced functionalities-including real-time data analysis, anti-tampering mechanisms, and emergency response capabilities-equip the system to address evolving security threats. The study emphasizes that incorporating Event-B strengthens ATM security infrastructure and ensures compliance with strict regulatory standards. This holistic approach provides a resilient, comprehensive defence against modern security challenges, safeguarding users, transactions, and financial institutions alike.

关键词： Event-B Framework RODIN ATM Security System Incremental System Design Mathematical Modelling Commercial Security

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Patient Privacy information Retrieval with Longformer and CRF, Followed by Rule-Based Time information Normalization: A Dual-Approach Study

Patient Privacy Information Retrieval with Longformer and CR...

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2024 International Workshop on Deidentification of Electronic Medical Record Notes

作者： Tseng, Fan-Pin Ko, Han-Chun Hou, Xiu-Yu Wijaya, Danang Chang, Connyn Kang-Lin Tsai, Richard Tzong-Han Natl Cent Univ 300 Zhongda Rd Taoyuan 320 Taiwan Natl Atom Res Inst 1000 Wenhua Rd Taoyuan 32546 Taiwan

ISBN: (纸本)9789819779659;9789819779666

This study explores integrating the Longformer model with Conditional Random Fields (CRF) for enhancing Named Entity Recognition (NER) in the domain of healthcare data processing. It specifically focuses on patient privacy information retrieval and time information normalization, utilizing the comprehensive 'Artificial Intelligence CUP 2023: Privacy Protection and Medical Data Standardization Challenge Dataset'. This research is conducted within the context of the AI CUP 2023 competition, which is dedicated to the privacy protection and standardization of medical data. Our approach utilized the Longformer model, renowned for its effectiveness in handling extensive text sequences, and combined it with CRF to enhance entity recognition accuracy in Electronic Health Record (EHR) text notes. To tackle challenges such as lengthy texts and class distribution imbalances, we developed a specialized process for managing large-scale textual data. This involved segmenting extensive texts into manageable chunks of 4,096 characters, which allowed for more focused and efficient training. For prediction, we employed a sliding window technique to ensure seamless integration and analysis of these text segments. This strategy was crucial in accurately retrieving patient privacy information from lengthy healthcare records. Additionally, our methodology included the implementation of rule-based methods for time information normalization, further enhancing the applicability of our approach in the medical data domain. The combination of Longformer and CRF has proven effective in accurately identifying sensitive patient information and normalizing time-related data. This approach illustrates the synergy between deep learning models and traditional methods, showcasing a robust framework for enhancing the security and efficiency of healthcare data processing.

关键词： Healthcare Named Entity Recognition Longformer-CRF EHR Text Notes Processing

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Region of Interest Features and Classification of MRI Brain Lesions 12th

Region of Interest Features and Classification of MRI Brain ...

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12th Ecuadorian Conference on information and Communication Technologies

作者： Castillo, Darwin Alejandro, Ricardo J. Garcia, Santiago Rodriguez-Alvarez, Maria Jose Lakshminarayanan, Vasudevan Univ Tecn Facultad Ciencias Exactas & Natur Dept Quim Particular Loja Loja 1101608 Ecuador Univ Ghent Dept Expt Psychol Ghent Belgium Univ Politecn Valencia Inst Instrumentac Imagen Mol i3M Consejo Super Invest Cient CSIC Valencia Spain Univ Waterloo Theoret & Expt Epistemol Lab Sch Optometry & Vision Sci Waterloo ON Canada Univ Waterloo Dept Phys Elect & Comp Engn & Syst Design Engn Waterloo ON Canada

ISBN: (纸本)9783031754302;9783031754319

Nowadays, the diagnosis of numerous diseases is facilitated by medical imaging. In that context, the identification of brain lesions presented as White Matter Hyperintensities (WHMs) and their related diseases is essential to have a correct diagnosis. Machine- and deep learning (subfields within artificial intelligence) could support the diagnosis (especially in complex medical images) by leveraging the structure and regularities within the imaging data. This project presents a technique for the classification of WHMs concerning ischemia and demyelination through the analysis of the region of interest (ROI) features of magnetic resonance images. To do that, we analyzed radiomic features using a combination of principal component analysis (PCA) and support vector machine (SVM) classification. Next, we used a transfer learning fine-tuned ResNet18 model to more thoroughly analyze and classify lesioned ROIs. For that, we used patient data alone and additional synthetic data (generated using spectral generative adversarial networks -SNGAN). The results show an accuracy mean value of 0.96 without data augmentation;while we had a value of 0.54 using synthetic data, a similar value was acquired with radiomics-informed SVM classification (0.56). These findings constitute a starting point for future projects exploring different ways of informing and fine-tuning artificial intelligence models to detect, classify, and segment MRI pathologies characterized by small lesions.

关键词： RoIs brain lesions radiomics GAN resnet18 machine learning medical images

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Feeling Lonely-with 20 Others: The Visualization of Loneliness Using Gen AI and Digital Narratives 1

引用

15th Indian Conference on Human-computer Interaction Design and Research

作者： Mane, Anisha Sir JJ Sch Art Mumbai Maharashtra India

ISBN: (数字)9783031808326

ISBN: (纸本)9783031808319;9783031808326

The average person goes through 400 emotional experiences every day. We often categorize them to understand our emotional spectrum better. However, this categorization creates stereotypes that can limit the scope of representation. Misdiagnosis of emotions based on such stereotypes can lead to misdiagnosis of the symptoms of more severe mental health issues. In this paper, we have explored possible solutions to such misrepresentations through the personalized creations of the visuals of the emotion of loneliness using Generative AI. This has been done in an attempt to include more holistic visuals of various emotions. A digital platform has also been created, allowing viewers to access the library of stories and AI-generated images of emotions collected during research, Personally created for each story. They can submit their own stories to the collection which will be transformed into AI Images. This participatory model not only democratizes the process of emotional visualization but also fosters a community of shared experiences. Ultimately, 'Emotional Windows' highlights the potential of AI and digital platforms to transform how we perceive and engage with mental health, paving the way for more personalized and meaningful interventions in the future.

关键词： Artificial Intelligence Psychology Loneliness

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Automated Fish Detection in Underwater Environments: Performance Analysis of YOLOv8 and YOLO-NAS 5th

Automated Fish Detection in Underwater Environments: Perform...

引用

5th Southern African Conference for Artificial Intelligence Research

作者： Chieza, Kenneth Brown, Dane Connan, James Salie, Daanyaal Rhodes Univ Dept Comp Sci Makhanda South Africa

ISBN: (纸本)9783031782541;9783031782558

Accurately determining fish populations is crucial for marine science, aiding in species tracking, fish behaviour studies, and sustainable aquaculture practices. This study evaluates the effectiveness of automated fish detection using You Only Look Once (YOLO) models, particularly YOLOv8 and YOLO-NAS, using benchmark datasets (DeepFish and OzFish) and two novel datasets from the South African Institute for Aquatic Biodiversity (SAIAB). The results indicate that YOLOv8 models consistently outperform YOLO-NAS models, effectively addressing challenges like poor image quality and varying environmental conditions. This study concludes that YOLOv8 models are better suited for underwater fish detection, offering robust performance and highlighting the potential of advanced deep learning models in enhancing ecological studies and sustainable fishery management.

关键词： Fish Detection computer Vision Deep Learning

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Clothes Image Retrieval via Learnable FashionCLIP 5th

Clothes Image Retrieval via Learnable FashionCLIP

引用

5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Sun, Yuan Zhao, Mingbo Donghua Univ Coll Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9789819770007;9789819770014

With the rapid development of e-commerce platforms and the continuous influx of massive clothing data such as the Internet and mobile apps, clothing has become an indispensable way of consumption in people's daily lives. Clothes image retrieval is especially useful when the user is interested in a cloth that emerged in social media videos (Instagram, TikTok), and this is a so-called video-to-shop retrieval task, where the target clothes and query clothes dressed in a short video are within different domains. In this paper, to handle the above problem, we propose a cross-domain clothing retrieval method based on massive data in the context of e-commerce live streaming. Our method includes two components: object detection and re-identification (Re-ID). The object detection branch is an anchor-free detector. The Re-ID branch is pre-trained by large-scale image-text pairs and prompts to learn joint embedding space, cross-domain, and fine-grained information for improving retrieval performance. This network enhances visual representation by integrating prompt learning and capitalizing on the cross-modal descriptive capability of FashionCLIP. We conducted experiments on a large-scale multimodal Watch and Buy dataset and achieved good results.

关键词： Clothing Retrieval Contrastive Learning Pre-trained Model Cross-modal Retrieval

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Enhanced Semantic Understanding with Graph-Based information Retrieval 1st

Enhanced Semantic Understanding with Graph-Based Information...

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1st International Workshop on Graph-Based Approaches in information Retrieval

作者： De Filippis, Giovanni M. Rinaldi, Antonio M. Russo, Cristiano Tommasino, Cristian Univ Naples Federico II Dept Elect Engn & Informat Technol Via Claudio 21 I-80125 Naples Italy Univ Naples Federico II Interdept Res Ctr Management & Innovat Healthcare Naples Italy

ISBN: (纸本)9783031713811;9783031713828

Traditional information retrieval systems, primarily based on keyword searches, often fail to capture nuanced domain-specific relationships. To address such an issue, we propose using a semantic graph-based approach, which enables enhanced semantic querying capabilities and contextual data retrieval. By mapping entities and their complex interdependencies within a graph, our system allows for sophisticated querying beyond simple keyword matches. It effectively leverages interconnected data to provide contextually relevant responses to complex queries, thereby improving the accuracy and depth of information retrieval. We have effectively demonstrated our solution in the field of nutrigenomics, highlighting how semantic graphs can significantly improve data interpretation and facilitate the creation of highly personalized nutrition plans and therapeutic interventions based on comprehensive and detailed genetic insights. Our code is publicly available on GitHub at https://***/CosmoIknosLab/bertopic_graph.

关键词： information Retrieval Semantic Graphs Semantic Search Nutrigenomics

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