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Multi-head attention graph convolutional network model:End-to-end entity and relation joint extraction based on multi-head attention graph convolutional network

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 468-477页

作者： Zhihua Tao Chunping Ouyang Yongbin Liu Tonglee Chung Yixin Cao School of Computer Science University of South ChinaHengyangChina Department of Computer Science and technology Tsinghua UniversityBeijingChina School of Computer and Information Systems Singapore Management UniversitySingaporeSingapore

At present,the entity and relation joint extraction task has attracted more and more scholars'attention in the field of natural language processing(NLP).However,most of their methods rely on NLP tools to construct dependency trees to obtain sentence structure *** adjacency matrix constructed by the dependency tree can convey syntactic *** trees obtained through NLP tools are too dependent on the tools and may not be very accurate in contextual semantic *** the same time,a large amount of irrelevant information will cause *** paper presents a novel end-to-end entity and relation joint extraction based on the multihead attention graph convolutional network model(MAGCN),which does not rely on external *** generates an adjacency matrix through a multi-head attention mechanism to form an attention graph convolutional network model,uses head selection to identify multiple relations,and effectively improve the prediction result of overlapping *** authors extensively experiment and prove the method's effectiveness on three public datasets:NYT,WebNLG,and *** results show that the authors’method outperforms the state-of-the-art research results for the task of entities and relation extraction.

关键词： information retrieval natural language processing

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Artificial Fish Swarm for Multi Protein Sequences Alignment in Bioinformatics

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computers, Materials & Continua 2022年第9期72卷 6091-6106页

作者： Medhat A.Tawfeek Saad Alanazi A.A.Abd El-Aziz Department of Computer Science College of Computer and Information SciencesJouf UniversitySaudi Arabia(KSA Department of Computer Science Faculty of Computers and InformationMenoufia UniversityEgypt Department of Information Systems College of Computer and Information SciencesJouf UniversitySaudi Arabia(KSA) Department of Information Systems and Technology Faculty of Graduates Studies and ResearchCairo UniversityEgypt

The alignment operation between many protein sequences or DNAsequences related to the scientific bioinformatics application is very *** is a trade-off in the objectives in the existing techniques of MultipleSequence Alignment (MSA). The techniques that concern with speed ignoreaccuracy, whereas techniques that concern with accuracy ignore speed. Theterm alignment means to get the similarity in different sequences with highaccuracy. The more growing number of sequences leads to a very complexand complicated problem. Because of the emergence;rapid development;anddependence on gene sequencing, sequence alignment has become importantin every biological relationship analysis process. Calculating the numberof similar amino acids is the primary method for proving that there is arelationship between two sequences. The time is a main issue in any alignmenttechnique. In this paper, a more effective MSA method for handling themassive multiple protein sequences alignment maintaining the highest accuracy with less time consumption is proposed. The proposed method dependson Artificial Fish Swarm (AFS) algorithm that can break down the mostchallenges of MSA problems. The AFS is exploited to obtain high accuracyin adequate time. ASF has been increasing popularly in various applicationssuch as artificial intelligence, computer vision, machine learning, and dataintensive application. It basically mimics the behavior of fish trying to getthe food in nature. The proposed mechanisms of AFS that is like preying,swarming, following, moving, and leaping help in increasing the accuracy andconcerning the speed by decreasing execution time. The sense organs that aidthe artificial fishes to collect information and vision from the environmenthelp in concerning the accuracy. These features of the proposed AFS make thealignment operation more efficient and are suitable especially for large-scaledata. The implementation and experimental results put the proposed AFS as afirst choice in th

关键词： Multiple sequence alignment swarm intelligence artificial fish swarm protein sequences

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Interactive Chatbot for Improving the Text Classification Data Quality 1

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15th International Conference on Interactive Mobile Communication, Technologies, and Learning, IMCL 2023

作者： Elzanfaly, Doaa S. Mohamed, Nada Amr Othman, Nermin Abdelhakim Department of Information Systems Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Department of Information Systems Faculty of Informatics and Computer Science British University in Egypt Cairo Egypt

ISBN: (数字)9783031560750

ISBN: (纸本)9783031560743

The pandemic is affecting the global community in many ways. In most developing countries, there is a limitation in the detection facilities, which affect many suspected cases. This paper proposes a chatbot framework to assist and provide guide for the suspected/infected patients with COVID-19. Conversational software agents activated by natural language processing is known as chatbot, are an excellent example of such machine. Our COVID Bot is based on an integrated model between the rule-based model and the class classification model, having the rule-based model integrated with the MongoDB NoSQL database. Chatbot, using Natural Language Processing (NLP) and data mining techniques to assist patients by providing immediate answers for their questions. It also acts as a novel communication mean for impaired people for sharing knowledge and information, through conversing with them. Based on the literature review, this paper compared our methods with three classical classification algorithms: random forest, gradient boosting, and multi-layer perceptron (MLP). Experimental results show that our proposed chatbot greatly improves the classification performance, with IE-Net as 94.80%, 92.79% as recall, 92.97% as precision and 94.93% as AUC for distinguishing COVID-19 cases from non-COVID-19 cases (with only common clinical diagnose data). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Coronavirus

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A New Hybrid Model for Segmentation of the Skin Lesion Based on Residual Attention U-Net

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computers, Materials & Continua 2023年第6期75卷 5177-5192页

作者： Saleh Naif Almuayqil Reham Arnous Noha Sakr Magdy M.Fadel Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and Technology(DHIET)MansouraEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion *** paper presents a novel framework for segmenting the *** framework contains two main stages:The first stage is for removing different types of noises from the dermoscopic images,such as hair,speckle,and impulse noise,and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network(U-Net).The framework uses variational Autoencoders(VAEs)for removing the hair noises,the Generative Adversarial Denoising Network(DGAN-Net),the Denoising U-shaped U-Net(D-U-NET),and Batch Renormalization U-Net(Br-U-NET)for remov-ing the speckle noise,and the Laplacian Vector Median Filter(MLVMF)for removing the impulse *** the second main stage,the residual attention u-net was used for *** framework achieves(35.11,31.26,27.01,and 26.16),(36.34,33.23,31.32,and 28.65),and(36.33,32.21,28.54,and 27.11)for removing hair,speckle,and impulse noise,respectively,based on Peak Signal Noise Ratio(PSNR)at the level of(0.1,0.25,0.5,and 0.75)of *** framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of *** experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure(SSIM)and PSNR and in the segmentation process as well.

关键词： Skin tumor speckle noise impulse noise hair noise deep learning segmentation

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State-of-the-art in human activity recognition based on inertial measurement unit sensors: survey and applications

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International Journal of computers and Applications 2025年第1期47卷 1-16页

作者： Sepahvand, Majid Meqdad, Maytham N. Abdali-Mohammadi, Fardin Department of Computer Engineering Arak University Markazi Iran Intelligent Medical Systems Department Al-Mustaqbal University Babil Hillah Iraq Department of Computer Engineering and Information Technology Razi University Kermanshah Iran

Human activity recognition systems using wearable sensors is an important issue in pervasive computing, which applies to various domains related to healthcare, context aware and pervasive computing, sports, surveillance and monitoring, and the military. Three approaches can be considered for activity recognition: video sensor-based, physical sensor-based, and environmental sensor-based. This paper investigates the related work regarding the physical sensor-based approaches to motion processing. In this paper, a wide range of artificial intelligence models, from single classifications to methods based on deep learning, have been reviewed. The human activity detection accuracy of different methods, under natural and experimental conditions poses several challenges. These challenges cause problems regarding the accuracy and applicability of the proposed methods. This paper analyzes the methods, challenges, approaches, and future work. The goal of this paper is to establish a clear distinction in the field of motion detection using inertial sensors. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Active learning

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Leveraging Convolutional Neural Network and Explainable AI for Enhanced Pneumonia Detection 1

Leveraging Convolutional Neural Network and Explainable AI f...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Murugan, Ramalingam Patel, Harshita Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India

ISBN: (纸本)9798331520540

Advances in machine learning and computer vision have significantly improved the diagnostic capabilities of medical imaging. Convolutional Neural Networks (CNNs) have emerged as a crucial tool for image classification, particularly in detecting pneumonia from chest X-ray images. Despite their high accuracy, CNNs face challenges in clinical adoption due to their lack of interpretability. Medical practitioners require a clear understanding of the decision-making process to trust AI-generated diagnoses. To address the aforementioned challenge, we propose pneumonia classification model incorporating CNN with a combination of the model and Grad-CAM, for enhanced interpretability. Grad-CAM visually outlines critical regions, influencing the prediction, so the clinician can easily confirm AI-based diagnoses with a more confident and credible approach. It is exclusively for pediatric pneumonia, an accurate and explainable machine for early interventions that can also work in resource-scarce regions. Our research uses a set of 5,863 pediatric chest X-ray images, divided into Pneumonia and Normal classes. The dataset was acquired from Guangzhou Women and Children's Medical Center and divided into three subsets: training, validation, and test. By improving the accuracy and transparency of the diagnosis, this model can help bridge the gap between AI and the real world. This can help facilitate quicker and more reliable pneumonia diagnostics. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhanced Adaptive Brain-computer Interface Approach for Intelligent Assistance to Disabled Peoples

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computer systems Science & Engineering 2023年第8期46卷 1355-1369页

作者： Ali Usman Javed Ferzund Ahmad Shaf Muhammad Aamir Samar Alqhtani Khlood M.Mehdar Hanan Talal Halawani Hassan A.Alshamrani Abdullah A.Asiri Muhammad Irfan Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical Sciences and Information SystemsNajran UniversityNajran61441Kingdom of Saudi Arabia Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia

Assistive devices for disabled people with the help of Brain-computer Interaction(BCI)technology are becoming vital bio-medical *** with physical disabilities need some assistive devices to perform their daily *** these devices,higher latency factors need to be addressed ***,the main goal of this research is to implement a real-time BCI architecture with minimum latency for command *** proposed architecture is capable to communicate between different modules of the system by adopting an automotive,intelligent data processing and classification ***-sky mind wave device has been used to transfer the data to our implemented server for command ***-Net Convolutional Neural Network(TN-CNN)architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data *** collection and processing are the responsibility of the central integrated server for system load *** of implemented architecture and deep learning model shows excellent *** proposed system integrity level was the minimum data loss and the accurate commands processing *** training and testing results are 99%and 93%for custom model implementation based on *** proposed real-time architecture is capable of intelligent data processing unit with fewer errors,and it will benefit assistive devices working on the local server and cloud server.

关键词： Disable person electroencephalogram convolutional neural network brain signal classification

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BallTraj: Fast-Moving Ball Trajectory Tracking Using an Encoder–Decoder Network 3rd

BallTraj: Fast-Moving Ball Trajectory Tracking Using an Enco...

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3rd International Conference on Sports Engineering, ICSE 2023

作者： Somani, Aditya Malapati, Aruna Department of Computer Science & Information Systems BITS Pilani Hyderabad Campus Telangana Hyderabad500078 India

ISBN: (纸本)9789819782963

Ball trajectory data is a crucial piece of information that must be accurately tracked for tennis professionals. High-end systems like hawk eye are crucial for precise tennis ball trajectory tracking. However, their high cost restricts their accessibility. As a more cost-effective alternative, a single camera can be used, but it poses challenges due to the tennis ball's small size and rapid movement. In this study, we propose a solution to the challenges faced by low-resource tennis analysis systems that utilize a single camera. Specifically, we introduce a heat map-based encoder–decoder network with skip connections, which learns the trajectory patterns of a tennis ball, tracks its high-speed movement, and produces dependable data. Additionally, we present a synthetic data generation technique that enhances model accuracy by a certain percentage through the generation of relevant data. The proposed model was trained using over 15 k frames and another 13 k + generated frames from various lighting conditions, camera angles, and court surfaces. The model's performance was assessed using frames captured in different conditions, yielding a precision of 98.2% and a recall of 91.4%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network coding

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Cancer Ease: You Are Not Alone! 1

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8th International Conference on information and Communication Technology for Intelligent systems, ICTIS 2024

作者： Almoamary, Atheer Alsuwailem, Raghad Altawil, Ruba Alanazi, Samiah Alluhaidan, Ala Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia

ISBN: (数字)9789819757992

ISBN: (纸本)9789819757985

Objective to create an environment accessible through any device, that assesses in the ease of the journey of living with cancer disease. To integrate a human-centric, quality of life improvement view to the medical-centric view of cancer solutions and treatments. Methods For the development of the project, we followed the iterative waterfall method, a simple SDLC method that allows for continuous feedback and adjustments. Results This idea was designed to take advantage of technology and help people with cancer or those recovering from it, their symptoms need to be tracked by a doctor and reminded of appointments, medications, or chemotherapy sessions. It is expected to advance patient adherence to their overall well-being, and motivate them during the treatment journey. Conclusion "Cancer Ease" has been designed to empower individuals dealing with cancer by providing a user-friendly interface for information, personalized health tracking, easily accessible, and ultimately striving to enhance the quality of life throughout their journey with the disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

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ComFaaS: A Dynamic Approach to Edge and Cloud Computing with Function-as-a-Service 24

ComFaaS: A Dynamic Approach to Edge and Cloud Computing with...

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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Lee, Jaden Jinu Nava, Judah J. Lee, Hanku Computer Science & Information Systems Department Minnesota State University Moorhead MoorheadMN United States

ISBN: (纸本)9798400717161

The rapid evolution of cloud and edge computing has redefined how data-intensive applications are developed and deployed, with Function-as-a-Service (FaaS) playing a pivotal role in this transformation. FaaS provides a serverless model where functions are executed in response to specific events, offering developers automatic scalability, high availability, and reduced infrastructure management overhead. The latest release of ComFaaS brings substantial improvements in flexibility, scalability, security, and ease of use. It introduces a dynamic architecture that enables FaaS applications to be added and executed at runtime, without the need to modify the core system, streamlining feature integration and enhancing scalability. ComFaaS also includes dynamic load balancing, which intelligently distributes workloads between edge and cloud environments, ensuring that tasks always benefit from the most efficient computing resources available. This hybrid approach allows edge and cloud computing to complement each other, resulting in optimized performance tailored to the specific needs of each application. The fully functional release of ComFaaS now delivers a powerful and adaptable solution for modern FaaS deployments, offering a secure and scalable platform for both cloud and edge environments. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cloud platforms

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