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Shearlet Transform Based Multiscale Fusion Network for Image Super Resolution 7th

Shearlet Transform Based Multiscale Fusion Network for Image...

引用

8th Asia Pacific Web and Web-Age information Management Joint International Conference on Web and Big Data

作者： Wei, Wei Weihai Ocean Vocat Coll Weihai 264200 Shandong Peoples R China Weihai Big Data Intelligent Applicat Engn Technol Weihai 264200 Peoples R China

ISBN: (纸本)9789819600540;9789819600557

Recently, convolutional neural networks (CNNs) based methods have achieved great success in the field of image super-resolution. However, most methods produce over-smoothed outputs, and suffer from degradation for very low resolution images. We present an accurate CNN-based structure based on shearlet transform in this paper. Firstly, we propose a multi-scale fusion network (MSFN) to fully explore features from low resolution images. And then, we integrate shearlet transform to makeMSFN further get the texture details for super-resolved images. The experiments demonstrate that the proposed network achieves superior super resolution results and outperforms the state-of-the-art.

关键词： convolutional neural networks multi-scale fusion network (MSFN) super-resolution

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Anomaly Detection of Residential Electricity Consumption Based on Ensemble Model of PSO-AE-XGBOOST 5th

Anomaly Detection of Residential Electricity Consumption Bas...

引用

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

作者： Liu, Hao Shi, Jiachuan Fu, Rao Zhang, Yanling Shandong Jianzhu Univ Sch Informat & Elect Engn Shandong Key Lab Intelligent Bldg Technol Jinan 250101 Peoples R China

ISBN: (纸本)9789819770038;9789819770045

In modern society, the dependency on electricity is increasing, with electricity becoming a vital support for daily life, industrial production, and social development. However, incidents like electricity theft contribute to substantial energy consumption and economic losses, escalating the severity of the situation. To address this issue, a method based on the optimization of particle swarm optimization (PSO) and integration of autoencoder and extreme gradient boosting (XGBoost) was proposed in this paper for detecting abnormal electricity consumption in residential areas. Firstly, the user data was preprocessed to enhance data quality and model performance. Subsequently, the particle swarm optimization was utilized to adjust the hyperparameters of the autoencoder, thereby enhancing its performance. The particle swarm optimization searched for the optimal parameter combination in the parameter space of the autoencoder and utilized the found optimal parameters to train the autoencoder, thereby learning effective features from the data. Finally, using the features extracted by the autoencoder, XGBoost was employed as a supervised learning model for classification and anomaly detection, identifying users with abnormal electricity consumption. The results demonstrated that this model outperformed other detection methods such as support vector machines (SVM), K-nearest neighbors (KNN), and decision trees in terms of accuracy and recall. This research is significant for enhancing residential electricity security and energy utilization efficiency, and provides valuable insights for further research and practical applications in related fields.

关键词： Ensemble model Autoencoder XGBoost Anomaly detection

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A Bone Fracture Detection Using CNN Model 4th

A Bone Fracture Detection Using CNN Model

引用

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

作者： Nadia, Afsana Nuruzzaman, Md. Nomani, Khan Md. Shibli Billal, Md. Masum East West Univ Dhaka Bangladesh Hajee Mohammad Danesh Sci & Technol Univ Dinajpur Bangladesh Amer Int Univ Bangladesh Dhaka Bangladesh Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9783031821554;9783031821561

Accurate bone fracture detection can be critical in the medical diagnosis process. This paper proposes a bone fracture detection approach based on deep learning using Convolutional Neural Networks (CNNs) with X-ray images. We have explored three models: one predefined model (MNASNet1) and two custom models (Proposed Model 1 and Proposed Model 2). Model performance is evaluated using metrics such as F1-Score, precision, and accuracy. Results show that the proposed models achieved outstanding performance compared to the MNASNet1 model in most of the metrics. Specifically, Proposed Model 1 achieves the highest overall F1-score, recall, and precision (1.00) and also has the highest validation accuracy (99.89%). Our objective is to develop a custom CNN architecture that demonstrates the potential of our proposed models to enhance fracture detection accuracy and develop a web application to detect bone fractures faster to facilitate more precise clinical assessment and treatment decisions. Doctors can emphasize the benefits and explore collaboration opportunities in a relatable way to integrate our web application into medical practices.

关键词： Bone fracture detection CNN Deep learning X-ray image Image classification Biomedical application Medical image processing

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Ordinal and Position Enhance the Framework of the Multimodal Dialogue System 5th

Ordinal and Position Enhance the Framework of the Multimodal...

引用

5th China Intelligent Robotics Academic Conference

作者： Xu, Longmei Chen, Fei Li, Jinyu Dalian Maritime Univ Dalian Liaoning Peoples R China

ISBN: (纸本)9789819616138;9789819616145

Multimodal dialogue systems aim to process multimodal input information such as text and images simultaneously and then generate coherent and meaningful output dialogue responses. Although current methods have achieved notable progress, they have shortcomings in the following aspects: 1) Insufficient modeling of relationships between multimodal semantic elements, especially ignoring the interaction between the ordinal information in text and the position information of images. 2) Ineffectively integrating information from various modalities in multimodal conversations. To address these limitations, this paper proposes a framework for a multimodal dialogue system. We integrate both ordinal words from text and the position information of images as ordinal information to strengthen the interaction between multimodal semantic elements and to improve the understanding of user intent. In particular, in this framework, we incorporated the self-attention mechanism into the multimodal factorized bilinear pooling (MFB) method, which captures long-range dependencies between modalities, enhancing the effectiveness of multimodal data fusion. Finally, we conducted comprehensive experiments on public multimodal dialogue datasets (MMConv and MMD) and demonstrated that our proposed framework outperforms other methods in the experiments.

关键词： Multimodal Dialogue System Multimodal Fusion Ordinal information Multimodal Factorized Bilinear Pooling

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An Improved Hybrid Recommendation Algorithm for Vehicle-Cargo Matching in the Logistics 5th

An Improved Hybrid Recommendation Algorithm for Vehicle-Carg...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Wang, Yiting Wei, Lifen Xian Univ Technol Sch Econ & Management Xian Peoples R China

ISBN: (纸本)9783031807749;9783031807756

This paper aims to optimize the vehicle-cargo matching of the logistics platform through the hybrid recommendation algorithm to achieve the effective matching between vehicle and cargo sources. This paper constructs a hybrid recommendation model taken into account the content-based recommendation algorithm and the item-based collaborative filtering algorithm. The content-based recommendation algorithm has advantages in matching the inherent attributes of vehicle and cargo sources, whereas the item-based collaborative filtering can deal with the problem of interest drift, and the advantages of these two algorithms are integrated in the hybrid model in this paper. The matching factors are pick out from the data of current logistics platforms, and the feedback competition method is applied for the calculation of the weights, and then the weight similarity is worked out for the recommendation. Multiple aspects are considered in the hybrid model, so as to satisfy the users by recommendation results, thereby achieving vehicle-cargo matching. This paper focuses on the matching of vehicles and cargoes in multiple dimensions, where the personalized and diversified comprehensive recommendation results are acquired to meet the needs of car owners and cargo owners.

关键词： Vehicle-cargo matching Hybrid recommendation algorithm Logistics Content-based recommendation algorithm

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Integrating eXplainable AI in Healthcare: A Web Application Framework for Advancing the One Health Paradigm 4th

Integrating eXplainable AI in Healthcare: A Web Application ...

引用

24th International Conference Web Engineering (ICWE)

作者： Danese, Danilo Di Noia, Tommaso Lombardi, Angela Lofu, Domenico Nazary, Fatemeh Sardone, Rodolfo Sorino, Paolo Politecn Bari Dept Elect & Informat Engn DEI Bari Italy Univ Liverpool Inst Life Course & Med Sci Liverpool Merseyside England

ISBN: (纸本)9783031751097;9783031751103

In today's complex world, the intricate connections between human health, socio-economic factors, and environmental conditions necessitate a holistic approach to health challenges. This paper highlights the critical need for the One Health paradigm, emphasizing a comprehensive framework that integrates human, economic, and environmental health considerations. By incorporating Explainable Artificial Intelligence (XAI) principles, this paper showcases a pioneering method to make the sophisticated insights derived from AI more accessible and actionable for a broad range of stakeholders, including healthcare professionals, environmental experts, and policy makers. Within the framework of the MISTRAL H2020 European project, we illustrate a healthcare model that employs XAI to demystify AI algorithms, thereby fostering trust and enhancing understanding among its users. This innovative approach not only improves clinical decision-making but also addresses environmental health issues by rendering the opaque decision-making processes ofMachine Learning (ML) models transparent and understandable. The framework's significance is underlined by its capacity to inform Health Impact Assessments (HIA) and predict disease risks, facilitating precise interventions and informed policy development. Furthermore, we propose a method for engineering and accessing this framework online, allowing users to engage directly with the system.

关键词： Explainable Artificial Intelligence Web Application Life science

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LANet: Lightweight Attention Network for Medical Image Segmentation 2nd

LANet: Lightweight Attention Network for Medical Image Segme...

引用

2nd International Conference on information Technologies and Their Applications

作者： Tang, Yi Pertsau, Dmitry Zhao, Di Kupryianava, Dziana Tatur, Mikhail Belarusian State Univ Informat & Radioelect Minsk BELARUS

ISBN: (纸本)9783031734199;9783031734205

Medical image segmentation plays a crucial role in quantitative analysis, clinical diagnosis and medical interventions. However, extracting valuable information from medical images is challenging due to the presence of different object types and scales, complex backgrounds and tissue similarities. To address these issues, LANet, a Lightweight Attention Network which incorporates an Efficient Fusion Attention (EFA) block and an Adaptive Feature Fusion (AFF) decoding block was presented in the paper. The EFA block enhances the model's feature extraction capability by capturing task-relevant information while reducing redundancy in channel and spatial locations. The AFF decoding block fuses the purified low-level features from the encoder with the sampled features from the decoder, enhancing the network's understanding and expression of input features. Additionally, the model adopts MobileViT as a lightweight backbone network with a small number of parameters, facilitating easy training and faster predictive inference. The efficiency of LANet was evaluated using four public datasets: kvasir-SEG, CVC-clinicDB, CVC-colonDB, and the Data science Bowl 2018. Experimental results demonstrate that LANet outperforms state-of-the-art methods in terms of intersection over union (mIoU) and Dice scores.

关键词： LANet Lightweight Attention Network Efficient Fusion Attention Block Adaptive Feature Fusion Decoding Block Attention Mechanism

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UKRAG: A Unified Knowledge Graph to Enhance Retrieval Augmented Generation Performance 5th

UKRAG: A Unified Knowledge Graph to Enhance Retrieval Augmen...

引用

5th International Symposium on Intelligent Computing Systems

作者： Alkouz, Akram Al-Saleh, Mohammed I. Alarabeyyat, Abdulsalam Bouchahma, Majed Higher Colleges Technol Comp Informat Syst Abu Dhabi U Arab Emirates Jordan Univ Sci & Technol Comp Sci Dept Irbid Jordan Balqa Appl Univ Software Engn Dept Salt Jordan

ISBN: (纸本)9783031829307;9783031829314

Retrieval Augmented Generation models have demonstrated significant potential in enhancing the performance and reducing hallucinations in Large Language Models by incorporating external knowledge sources. However, effectively utilizing both structured and unstructured data in Retrieval-Augmented Generation remains a challenge, as most existing methods retrieve only short, contiguous chunks from a retrieval corpus, limiting the holistic understanding of the overall document context and structure. This paper presents a novel approach to constructing a knowledge graph that seamlessly integrates structured and unstructured data to enhance the performance of Retrieval-Augmented Generation models. Our method involves first converting structured data into a graph model and then merging it with the document context found in unstructured text. This combined data is processed to build a Unified Knowledge graph, providing a rich, interconnected information source for the Retrieval-Augmented Generation model. To evaluate our approach and proposed model, we conducted extensive experiments on a variety of tasks, including question-answering and complex reasoning. The results demonstrate that our Unified knowledge Graph RAG model significantly outperforms traditional RAG models, achieving higher accuracy and better handling of intricate queries. Notably, on benchmarks such as QuALITY, our model shows substantial improvements in performance metrics. This research underscores the potential of integrating structured and unstructured data into a unified knowledge graph to advance the capabilities of RAG models, offering a promising direction for future developments in Artificial Intelligence and Natural Language Processing.

关键词： Knowledge graph

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Integration of Social Design with ICT to Promote Sustainability 1

引用

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

作者： Sharma, Anubha Sharma, Divy Jain, Vikshita Aastha Agarwal, Kanan Agarwal, Kinshuk Absolom, Anisha Ajay Kumar Garg Engn Coll Ghaziabad Uttar Pradesh India Indian Inst Foreign Trade Kakinada India Indian Inst Technol Hyderabad Sangareddy Telangana India

ISBN: (数字)9783031808326

ISBN: (纸本)9783031808319;9783031808326

This study will explore and combine information Communication Technology (ICT) with Social Design principles. The aim is to promote sustainable use of water through AquaAlert, a smart water management system. AquaAlert caters to monitoring real-time water usage, detecting water leakage, and providing actionable insights to users to reduce water consumption. A mixed-method approach was deployed during the research, using surveys, interviews, and tests to detect the effectiveness of the device and system. The findings revealed the true potential of AquaAlert, focusing on sustainability and promoting the United Nation's Sustainable Development Goals (SDGs) related to clean water and sustainable cities. The paper will include the user-centered design approach, development process, and research methodology implemented to promote responsible water usage.

关键词： Social Design information Communication Technology(ICT) Sustainable Development Goals Sustainability Behavioral Design

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Fake News Detection Using Large Language Models 4th

Fake News Detection Using Large Language Models

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

作者： Ramya, G. R. Yasaswani, S. Veda Harshitha, P. Bapathi, Archana Thanuja, G. Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9783031837920;9783031837937

The spread of fake news, particularly across social media and digital news platforms, poses a growing challenge due to its influence on public opinion and social stability. This study introduces a novel framework for fake news detection, leveraging Large Language Models (LLMs), specifically GPT-4, with multimodal data fusion and an external fact-checking module. Unlike conventional approaches that rely mainly on text, the proposed framework addresses the multimodal nature of misinformation by integrating textual and visual features captured through Convolutional Neural Networks (CNNs) for images to detect misleading content more effectively. Additionally, the framework incorporates real-time fact-checking by cross-referencing statements with trusted sources, such as Wikipedia and ***, reducing false positives and model hallucinations. This approach beats current methods, offering a more thorough solution to the challenging problem of disinformation detection, according to extensive testing on benchmark datasets. Future work will explore multilingual and real-time applications to enhance its adaptability and impact.

关键词： Artificial Intelligence Deep Learning Natural Language Processing Multimodal Fusion GPT-4 Large Language Models

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