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检索条件"机构=Department of Knowledge Engineering and Computer Science"

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Exploring the Role of Generative AI in Constructing knowledge Graphs for Drug Indications with Medical Context 15

Exploring the Role of Generative AI in Constructing Knowledg...

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15th International Conference on Semantic Web Applications and Tools for Health Care and Life sciences, SWAT4HCLS 2024

作者： Alharbi, Reham Ahmed, Umair Dobriy, Daniil Lajewska, Weronika Menotti, Laura Saeedizade, Mohammad Javad Dumontier, Michel Department of Computer Science University of Liverpool Liverpool United Kingdom School of Science and Technology University of Camerino Camerino Italy Institute for Data Process and Knowledge Management Vienna University of Economics and Business Vienna Austria Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway Department of Information Engineering University of Padua Padova Italy Department of Computer Science Linköping University Linköping Sweden Institute of Data Science Maastricht University Maastricht Netherlands

The medical context for a drug indication provides crucial information on how the drug can be used in practice. However, the extraction of medical context from drug indications remains poorly explored, as most research concentrates on the recognition of medications and associated diseases. Indeed, most databases cataloging drug indications do not contain their medical context in a machine-readable format. This paper proposes the use of a large language model for constructing DIAMOND-KG, a knowledge graph of drug indications and their medical context. The study 1) examines the change in accuracy and precision in providing additional instruction to the language model, 2) estimates the prevalence of medical context in drug indications, and 3) assesses the quality of DIAMOND-KG against NeuroDKG, a small manually curated knowledge graph. The results reveal that more elaborated prompts improve the quality of extraction of medical context;71% of indications had at least one medical context;63.52% of extracted medical contexts correspond to those identified in NeuroDKG. This paper demonstrates the utility of using large language models for specialized knowledge extraction, with a particular focus on extracting drug indications and their medical context. We provide DIAMOND-KG as a FAIR RDF graph supported with an ontology. Openly accessible, DIAMOND-KG may be useful for downstream tasks such as semantic query answering, recommendation engines, and drug repositioning research. © 2024 Copyright for this paper by its authors.

关键词： knowledge graph

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Reviewing Facial Insight - A Smart Approach to Identity Unveiling Through Active Facial Patches and Multi-Task Cascaded Convolutional Networks 5

Reviewing Facial Insight - A Smart Approach to Identity Unve...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kavin, F. Pradeepa, K. Selvalakshmi, B. Anitha, P. Subashini, K. Vijayalakshmi, P. Cyient Maharashtra Navi Mumbai India St. Michael College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Sivaganga India Tagore Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sai Renganathan Engineering College Department of ECE Tamil Nadu Coimbatore India Tagore Engineering College Dept. of Information Technology Tamil Nadu Chennai India Knowledge Institute of Technology Department of Computer Science and Engineering Salem Tamil Nadu Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

This research work proposes an innovative method for enhancing facial recognition accuracy and efficiency by leveraging active facial patches and a Multi-Task Cascaded Convolutional Network (MTCNN). The review assesses the paper's contributions, methodology, experimental results, and potential impact on advancing facial recognition technology. It highlights the strengths of the proposed approach, including its clear problem statement, thorough methodology, compelling experimental results, and potential for real-world application. Additionally, the review offers recommendations for improvement, such as further exploration of potential biases and scalability considerations. Overall, Facial Insight presents a promising advancement in identity unveiling within facial recognition systems, contributing to the broader field of biometrics and computer vision. © 2024 IEEE.

关键词： Biometrics

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Blockchain-Based IoMT Security: BSHS-EODL Method for Secure Data Storage and Analysis 3

Blockchain-Based IoMT Security: BSHS-EODL Method for Secure ...

引用

3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Rajak, Biru Shailaja, M. Al-Dabagh, Mustafa Zuhaer Nayef Mohammed, Ali Ali Saber Alazzam, Malik Bader Ponni Valavan, M. Asansol Girls' College Kazi Nazrul University Department of Computer Science Asansol India Hyderabad India College of Science Knowledge University Department of Computer Science Erbil Iraq Al Kitab University Department of Computer Engineering Kirkuk Iraq Jadara University Faculty of Information Technology Irbid Jordan K. Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798331508456

The integration of the Internet of Medical Things (IoMT) has revolutionized healthcare by improving patient care and system efficiency. However, this progress introduces significant concerns about data security and privacy, particularly with medical data. Traditional methods like Decision Tree, Random Forest, and Naïve Bayes face limitations in scalability, accuracy for large datasets, and energy efficiency. To address these challenges, this research introduces a novel Blockchain-based IoMT framework, BSHS-EODL (Blockchain-Secured Healthcare System with Edge-Oriented Deep Learning). The framework employs Long Short-Term Memory (LSTM) networks for precise healthcare data analysis, optimized using the Grey Wolf Optimization (GWO) algorithm for hyperparameter tuning. The proposed system achieved outstanding results, including an accuracy of 99.42%, precision of 99.51%, recall rate of 99.47%, and F1 score of 99.39%, outperforming conventional methods. Moreover, BSHS-EODL demonstrated remarkable efficiency, this approach not only enhances healthcare data security and diagnostic accuracy The system is implemented using Python integrated with MATLAB. © 2025 IEEE.

关键词： Patient treatment

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Face Recognition Based on Fusion Feature Extraction Algorithms 6

Face Recognition Based on Fusion Feature Extraction Algorith...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Albakri, Ibrahim Mahmood Rashid Ahmad, Muhammad Imran Md Isa, Mohd Nazrin Al-Dabagh, Mustafa Zuhaer Nayef Universiti Malaysia Perlis Faculty of Electronic Engineering Technology Perlis Malaysia Collage of Agriculture Telafer University Telafer Iraq Knowledge University Department of Computer Science Erbil Iraq

ISBN: (纸本)9798350306613

Biometric-based methods have emerged as a highly promising avenue for individual recognition, with face recognition standing out as a key application in image analysis. Facial recognition systems are affected by various image condition factors, including expressions, occlusion, poses, and illuminations. It is crucial to consider reasonable variations in illumination between gallery and probe images when developing face recognition algorithms. In the context of face verification, illumination variation significantly influences the likelihood of misclassification. Existing research lacks a focus on imaging conditions, especially regarding illumination, in face recognition systems. This research aims to identify and design a scheme to address illumination variation in facial recognition. The proposed scheme will undergo analysis and evaluation to assess its accuracy and effectiveness. The image processing in this research is divided into two phases. The first is the preprocessing phase, encompassing auto contrast balancing and noise reduction. The second phase is face processing, which involves illumination normalization, face detection, feature extraction, and face recognition. Two datasets are utilized: The first being the ORL dataset, which includes images with illumination variations, and the second being the Yale database, where images contain various noises. © 2024 IEEE.

关键词： FACE RECOGNITION Feature Extraction Fusion HOG LBP

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Enhancing Explainable Matrix Factorization with Tags for Multi-Style Explanations 15

Enhancing Explainable Matrix Factorization with Tags for Mul...

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15th International Conference on knowledge Discovery and Information Retrieval, KDIR 2023 as part of the 15th International Joint Conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2023

作者： Seton, Olurotimi Haghighi, Pegah Sagheb Alshammari, Mohammed Nasraoui, Olfa Knowledge Discovery and Web Mining Lab Computer Science and Engineering Dept University of Louisville United States Department of Computer Science Faculty of Computing and Information Technology Northern Border University Arar Saudi Arabia

ISBN: (纸本)9789897586712

Black-box AI models tend to be more accurate but less transparent and scrutable than white-box models. This poses a limitation for recommender systems that rely on black-box models, such as Matrix Factorization (MF). Explainable Matrix Factorization (EMF) models are "explainable" extensions of Matrix Factorization, a state of the art technique widely used due to its flexibility in learning from sparse data and accuracy. EMF can incorporate explanations derived, by design, from user or item neighborhood graphs, among others, into the model training process, thereby making their recommendations explainable. So far, an EMF model can learn a model that produces only one explanation style, and this in turn limits the number of recommendations with computable explanation scores. In this paper, we propose a framework for EMFs with multiple styles of explanation, based on ratings and tags, by incorporating EMF algorithms that use scores derived from tag-centric graphs to connect rating neighborhood-based EMF techniques to tag-based explanations. We used precalculated explainability scores that have been previously validated in user studies that evaluated user satisfaction with each style individually. Our evaluation experiments show that our proposed methods provide accurate recommendations while providing multiple explanation styles, without sacrificing the accuracy of the recommendations. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Matrix algebra

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Real-Time Myanmar Hand Gestures Recognition Using Deep Learning Models 8th

Real-Time Myanmar Hand Gestures Recognition Using Deep Learn...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Kyaw, Nwe Ni Mitra, Pabitra Sinha, G.R. Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal721302 India Indian Knowledge System Lab International Institute of Information Technology Bangalore Karnataka Bengaluru560100 India

ISBN: (纸本)9789819738588

The proposed system for recognizing Myanmar sign language between individuals who are deaf. The aim of this study is to develop deep learning models for the purpose of accurately identifying dynamic hand gesture images depicting 14 classes of Myanmar words and numbers. The present system utilizes a new dataset comprising 7834 images for 14 classes obtained through Webcam. The objective of this study is to enhance the robustness of the new model. Next, we proceed to evaluate the proposed model by comparing them to those of existing convolutional neural network models. These models are generally recognized as effective deep-learning approaches for recognition tasks. The proposed model exhibits a performance level of 96% for both validation and testing accuracies. Furthermore, the experimental finding demonstrates that the model, convolutional layers, achieved an accuracy of 100% in recognizing Myanmar Sign Language (MSL). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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An Effective Emotion Recognition Method Using Facial and Speech Features 3

An Effective Emotion Recognition Method Using Facial and Spe...

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3rd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

作者： Muthumanickam, K. Mahesh, P.C.Senthil Vijayalakshmi, P. Department of Information Technology Tamilnadu Thottiyam India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam India Knowledge Institute of Technology Department of Computer Science and Engineering Tamilnadu Salem India

ISBN: (纸本)9781665456647

In face-To-face conversations, facial expressions help humans communicate nonverbally. Since the early 1990s, academics' focus on automatic facial expression detection, which is essential for human-machine interfaces, has grown. Traditional machine learning techniques can demand a difficult feature extraction procedure and yield subpar results. Using current developments in deep learning, we present efficient deep Convolutional Neural Networks (CNNs) that can automatically and accurately evaluate the semantic information contained in faces and speech without manually defining feature descriptors. Additionally, in order to develop an effective CNNs based emotion recognition system with a high degree of classification capability, we employ various loss functions and training techniques. The experimental results demonstrate that, the proposed method outperforms existing solutions in terms of performance and makes it ideal for real-Time systems. © 2022 IEEE.

关键词： Emotion Recognition

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MKGL: Mastery of a Three-Word Language 38

MKGL: Mastery of a Three-Word Language

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Guo, Lingbing Bo, Zhongpu Chen, Zhuo Zhang, Yichi Chen, Jiaoyan Lan, Yarong Sun, Mengshu Zhang, Zhiqiang Luo, Yangyifei Li, Qian Zhang, Qiang Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Ant Group Joint Lab of Knowledge Graph China Ant Group China Department of Computer Science The University of Manchester United Kingdom School of Computer Science and Engineering Beihang University China School of Computer Science Beijing University of Posts and Telecommunications China School of Software Technology Zhejiang University China

Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs), which describe facts in the form of triplets and allow minimal hallucinations, remains an underexplored frontier. In this paper, we investigate the integration of LLMs with KGs by introducing a specialized KG Language (KGL), where a sentence precisely consists of an entity noun, a relation verb, and ends with another entity noun. Despite KGL's unfamiliar vocabulary to the LLM, we facilitate its learning through a tailored dictionary and illustrative sentences, and enhance context understanding via real-time KG context retrieval and KGL token embedding augmentation. Our results reveal that LLMs can achieve fluency in KGL, drastically reducing errors compared to conventional KG embedding methods on KG completion. Furthermore, our enhanced LLM shows exceptional competence in generating accurate three-word sentences from an initial entity and interpreting new unseen terms out of KGs. © 2024 Neural information processing systems foundation. All rights reserved.

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Comprehensive Approaches to Securing Multi-Cloud Architectures: Best Practices and Emerging Solutions 7

Comprehensive Approaches to Securing Multi-Cloud Architectur...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Nair, Raveena R Sreevidya, D. Mohan, Chinnem Rama Banerjee, Joydeep Chouhan, Kuldeep Dharamvir Saveetha School of Law Chennai India and Ai & Ds Hyderabad India Narayana Engineering College Nellore Department of Computer Science and Engineering Andhra Pradesh India Nshm Knowledge Campus Durgapur West Bengal Kolkata India Sharda School of Engineering and Technology Department of Computer Science Applications Uttar Pradesh Greater Noida India The Oxford College of Engineering Department of Mca Bommanahalli Karnataka Bengaluru India

ISBN: (纸本)9798350350067

With an emphasis on multi-cloud networks, this research paper offers a synopsis of cloud computing techniques. It covers the fundamentals of cloud programming, the difficulties that cloud computing faces, and the ways that multi-cloud systems, such as cloud hybridization, federation clouds, cross-clouds, and multi-clouds, might help solve these problems. The opportunity of multi-cloud structures in combination with modern technologies such as Big Data and also Machine Learning (ML) is explored as well in the study, leading to some fascinating breakthroughs. To assist researchers, grasp the difficulties and generate enthusiasm within the community for the upcoming pairing of multi-cloud services with additional cutting-edge technologies, the aim is to present a contemporary perspective on multi-cloud technology and solve outstanding concerns. © 2024 IEEE.

关键词： Cloud platforms

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2.5D ASF-UNet: Adjacent Slice Spatial Feature Fusion Model for WMH Segmentation from 3D MR Brain Image

2.5D ASF-UNet: Adjacent Slice Spatial Feature Fusion Model f...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Huang, Lan Sun, Yinglu Zhao, Ziqi Guo, Chunjie Wang, Yan College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun China The First Hospital of Jilin University Department of Radiology Changchun China

ISBN: (纸本)9798350386226

Segmenting brain white matter hyperintensities (WMH) from 3D Magnetic Resonance (MR) images is crucial for the diagnosis, treatment, and prognosis of Multiple Sclerosis (MS). Unlike common 2D images, this task is more challenging and time-consuming. Classical deep learning methods for 3D image segmentation face two main challenges: 1) Pure 3D networks have more parameters and are prone to overfitting. 2) When using 2D networks to segment slices of 3D images, the lack of 3D structural information results in suboptimal segmentation after reconstruction. To address these difficulties, we propose the 2.5D ASF-UNet, which employs the 2.5D workflow and uses adjacent slices as the input for 2D segmentation network ASF-UNet. In ASF-UNet, separate down-sampling paths are used for the adjacent slices, and the Local Spatial Attention Module (LSAM) is designed to more effectively integrate 3D spatial information into the 2D network. Additionally, the Conv-Spectral-Block (CSB) is designed to extract and integrate local and global features. It allows the model to capture global spatial structures while preserving detailed information. Experimental results on MICCAI MSSEG 2016 and Local MS datasets show that 2.5D ASF-UNet achieve better segmentation performance than other deep learning methods. © 2024 IEEE.

关键词： Image segmentation

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