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arXiv 2024年

作者： Averly, Reza Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States College of Pharmacy The Ohio State University United States

Clinical named entity recognition (NER) aims to retrieve important entities within clinical narratives. Recent works have demonstrated that large language models (LLMs) can achieve strong performance in this task. While previous works focus on proprietary LLMs, we investigate how open NER LLMs, trained specifically for entity recognition, perform in clinical NER. Our initial experiment reveals significant contrast in performance for some clinical entities and how a simple exploitment on entity types can alleviate this issue. In this paper, we introduce a novel framework, entity decomposition with filtering, or EDF. Our key idea is to decompose the entity recognition task into several retrievals of entity sub-types and then filter them. Our experimental results demonstrate the efficacies of our framework and the improvements across all metrics, models, datasets, and entity types. Our analysis also reveals substantial improvement in recognizing previously missed entities using entity decomposition. We further provide a comprehensive evaluation of our framework and an in-depth error analysis to pave future works. © 2024, CC BY.

关键词： Wiener filtering

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RMAvatar: Photorealistic Human Avatar Reconstruction from Monocular Video Based on Rectified Mesh-embedded Gaussians

arXiv

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arXiv 2025年

作者： Peng, Sen Xie, Weixing Wang, Zilong Guo, Xiaohu Chen, Zhonggui Yang, Baorong Dong, Xiao College of Computer Engineering Jimei University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China Department of Computer Science The University of Texas at Dallas Richardson United States School of Informatics Xiamen University Xiamen China Guangdong Provincial/Zhuhai Key Laboratory of IRADS BNU-HKBU United International College Zhuhai China

We introduce RMAvatar, a novel human avatar representation with Gaussian splatting embedded on mesh to learn clothed avatar from a monocular video. We utilize the explicit mesh geometry to represent motion and shape of a virtual human and implicit appearance rendering with Gaussian Splatting. Our method consists of two main modules: Gaussian initialization module and Gaussian rectification module. We embed Gaussians into triangular faces and control their motion through the mesh, which ensures low-frequency motion and surface deformation of the avatar. Due to the limitations of LBS formula, the human skeleton is hard to control complex non-rigid transformations. We then design a pose-related Gaussian rectification module to learn fine-detailed non-rigid deformations, further improving the realism and expressiveness of the avatar. We conduct extensive experiments on public datasets, RMAvatar shows state-of-the-art performance on both rendering quality and quantitative evaluations. Please see our project page at https://***. Copyright © 2025, The Authors. All rights reserved.

关键词： Mesh generation

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Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, ScienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

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BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Lan, Libin Cai, Pengzhou Jiang, Lu Liu, Xiaojuan Li, Yongmei Zhang, Yudong College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China College of Computer Science Chongqing University Chongqing400044 China College of Artificial Intelligence Chongqing University of Technology Chongqing401135 China Department of Radiology The First Affiliated Hospital of Chongqing Medical University Chongqing400016 China Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia School of Computer Science and Engineering Southeast University Jiangsu Nanjing210096 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom

Accurate medical image segmentation plays an essential role in clinical quantification, disease diagnosis, treatment planning and many other applications. Both convolution-based and transformer-based u-shaped architectures have made significant success in various medical image segmentation tasks. The former can efficiently learn local information of images while requiring much more image-specific inductive biases inherent to convolution operation. The latter can effectively capture long-range dependency at different feature scales using self-attention, whereas it typically encounters the challenges of quadratic compute and memory requirements with sequence length increasing. To address this problem, through integrating the merits of these two paradigms in a well-designed u-shaped architecture, we propose a hybrid yet effective CNN-Transformer network, named BRAUNet++, for an accurate medical image segmentation task. Specifically, BRAU-Net++ uses bi-level routing attention as the core building block to design our u-shaped encoder-decoder structure, in which both encoder and decoder are hierarchically constructed, so as to learn global semantic information while reducing computational complexity. Furthermore, this network restructures skip connection by incorporating channel-spatial attention which adopts convolution operations, aiming to minimize local spatial information loss and amplify global dimension-interaction of multi-scale features. Extensive experiments on three diverse imaging modalities datasets demonstrate that our proposed approach outperforms other state-of-the-art methods including its baseline: BRAU-Net under almost all evaluation metrics, which reveals the generality and robustness of our approach for multi-modal medical image segmentation tasks. The code and models are publicly available on GitHub. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Target-Oriented WiFi Sensing for Respiratory Healthcare: from Indiscriminate Perception to In-Area Sensing

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IEEE Network 2024年

作者： Wang, Meng Huang, Jinyang Zhang, Xiang Liu, Zhi Li, Meng Zhao, Peng Yan, Huan Sun, Xiao Dong, Mianxiong Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligence Machine School of Computer and Information Hefei230601 China University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Hefei230026 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan Guizhou Normal University School of Big Data and Computer Science Guiyang550025 China Muroran Institute of Technology Department of Sciences and Informatics 0508585 Japan

Driven by the vision of integrated sensing and communication (ISAC) toward 6G technology, the WiFi-based respiration sensing approach has emerged as a highly competitive candidate for advanced healthcare services. Nevertheless, the indiscriminate perception of all the moving objects within the sensing area raises challenges for system stability and robustness for real-world deployment, especially for the susceptibility to motion interference from other people. Meanwhile, thanks to the emerging in-area wireless sensing technologies, i.e., beamforming, the dynamic environment tolerance of these technologies gives crucial opportunities for target-oriented WiFi-based healthcare services. This article discusses the compelling intersection of in-area WiFi sensing and the future of target-oriented intelligent respiratory healthcare network services. We first present a novel WiFi-based healthcare-assisting framework tailored for inter-user motion interference-tolerable (IMIT) sensing. The detailed components of low-level signal preprocessing, target-direction signal extraction, and healthcare-related services are discussed. By utilizing CSI beamforming technology, which adjusts a directional beam toward the desired direction and adaptively places beam nulls in noisy directions, we can mitigate motion interference and achieve target-oriented healthcare sensing. Finally, we discuss open challenges and potential solutions for target-oriented WiFi healthcare sensing. © 1986-2012 IEEE.

关键词： Wireless local area networks (WLAN)

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

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IEEE International Conference on Data Mining (ICDM)

作者： Yifan Zhang Haolong Xiang Xuyun Zhang Xiaolong Xu Wei Fan Qin Zhang Lianyong Qi School of software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia Uffield Department of Women's & Reproductive Health University of Oxford England College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering University of Petroleum (East China) China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods.

关键词： Learning systems Training Accuracy Clustering algorithms Forestry Network security Fraud Time complexity Parallel algorithms Anomaly detection

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Storage Matched Systems for Single-Click Photo Recognition Using CNN

Storage Matched Systems for Single-Click Photo Recognition U...

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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Sunil Kumar R Shantha Mary Joshitta Deepak Dasaratha Rao Harinakshi Syeda Masarath Vivek Nivruttira Waghmare Department of Computer Science And Engineering School of Computing Mohan Babu University (erstwhile Sree Vidyanikethan Engineering College) Tirupathi AP India Jayaraj Annapackiam college for women (Autonomous) Periyakulam Computer Science Mother Teresa Women's University Kodaikanal Tamilnadu India Consultant R&D City Plano USA Computer Science St Philomenas College (Autonomous) Mysore India

During this research, we provide two novel concepts: learners to acquire network characteristics for the unlabelled pictures over the spot in one-shot education, while supplementing the CNNs using memories. We particularly introduce Memories Matched Networking (MM-Net), a revolutionary deep design that investigates learning under the premise that learning and testing circumstances require being identical. In order to fully utilise the information contained in the collection, MM-Net essentially stores the characteristics of a collection of named pictures (the reference group) into memories and returns from memories while doing inferences. A Cognitive Learner, however, uses the storage spaces sequentially in predicting the CNN settings for unlabelled pictures. Whenever just a few instances from every category are shown at each stage while the instruction is switched across mini-batch into mini-batch, the entire system is taught. This is designed for quick learning whenever merely a few instances representing novel groups are displayed during testing. In contrast to traditional one-shot training techniques, our own MM-Net might generate a single, cohesive network regardless of the quantity of photos or classes. Using 2 open records, Omniglot while miniImageNet, thorough analyses are undertaken, and better outcomes are presented when contrasted with cutting-edge methods. Furthermore, the privilege of MM-Net raises one-shot performance on Omniglot via 98.95% into 99.28% as well as miniImageNet via 49.21% into 53.37%.

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Analysis of Lung Cancer for Developing Smart Healthcare with the Help of BGWO Based TSA-XGBoost Model

Analysis of Lung Cancer for Developing Smart Healthcare with...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Mahaveerakannan R Murali Dhar M S Sohan Goswami P. Gopala Krishna K. Jayanthi M. Sathish Kumar Department of Spatial Informatics The Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu Department of Computer Science and Technology Technique Polytechnic Institute West Bengal Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana Department of Computer Science and Engineering Pollachi Institute of Engineering and Technology Coimbatore Pollachi Tamil Nadu Department of Cyber Security Karpagam college of Engineering Coimbatore Tamil Nadu

Given the circumstances the healthcare system, the Internet of Things (IoT) is crucial. IoT gadgets offer patient data for the framework of healthcare monitoring. IoT is a key component in every aspect of the health care management system since people can use smart devices to check on their health. Lung cancer is a fatal malignancy, and the likelihood of survival is increased by early identification. Because of the computational difficulty involved in gathering characteristics, it is imperative to design an approach using machine learning techniques for categorising cancer disease because the classification results given by the current methods are inadequate. With the use of the synthetic minority oversampling methods (SMOTE) methodology, the incoming data is pre-processed and balanced. With the help of the binary grey wolf optimisation algorithm (BGWOA), the pertinent features are best chosen. Finally, the suggested model's hyper-parameters are best chosen by the tunicate swarm optimisation (TSA) model, and the classification is carried out by the extreme gradient boosting (XGBoost) model. The experimental analysis demonstrates that the suggested model attained accuracy and recall values of 98% and 95%, respectively, compared to 95% and 95%, respectively, for the identical proposed model without the feature selection (FS) method.

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AI Technological advancements in the design of intangible cultural products and Preservation of Cultural Heritage Sites

AI Technological advancements in the design of intangible cu...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Agarwal, Priyam Swami, Siddharth Iqbal, Mohammed Ismail Rawat, Divya Joshi, Lalit Mohan Aluvala, Srinivas Uttaranchal University Uttranchal School of Journalism and Mass Communication Dehradun India Doon University School of Environment and Natural Resources Dehradun India University of Technology and Sciences College of Engineering and Technology Nizwa Oman Uttaranchal University Uttaranchal School of Computing Science Dehradun India Dr. A.P.J. Abdul Kalam Institute of Technology CSE Department Tanakpur India SR University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350394412

Culture plays a significant role in shaping audience's behavior, especially in this digitalized era, where large portion of any individual's day is spent online resulting in building global integration of people belonging to different cultures. Therefore, Cultural Products and Heritage Sites are also equally important as it holds a major part of any country's glorious and rich history and can earn recognition at a global level for any country. The main aim of this paper was to identify latest advancements in 4.0 enabling technologies such as Artificial Intelligence (AI), Virtual Reality (VR) and big data in innovatively digitally designing intangible cultural products and measures for ensuring long term sustainability of Cultural Heritage Sites (CHS). This paper has also given some future recommendations for the smooth management and establishing creative digital data base set design for CHS. © 2024 IEEE.

关键词： Artificial Intelligence Culture Culture Heritage Sites Intangible Cultural Products

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Code Refactoring for Software Reusability: An Experimental Study

Code Refactoring for Software Reusability: An Experimental S...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdullah Almogahed Hairulnizam Mahdin Mazidah Mat Rejab Abdulwadood Alawadhi Samera Obaid Barraood Manal Othman Omar Al-Jamili Abdulwahab Ali Almazroi Shazlyn Milleana Shaharudin Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Malaysia InterNetWorks Research Laboratory School of Computing Universiti Utara Malaysia Sintok Malaysia College of Computers and Information Technology Hadhramout University Hadhramout Yemen Computer Science Department College of Applied Science Taiz University Taiz Yemen Faculty of Engineering and Computing University of Science and Technology Yemen Department of Information Technology College of Computing and Information Technology at Khulais University of Jeddah Jeddah Saudi Arabia Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Malaysia

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Refactoring plays a critical role in enhancing the quality of software systems, enabling them to accommodate growing user bases, feature sets, and performance demands. Software reusability is an essential aspect of software quality. It is crucial in modern software development as it offers several benefits, including increased productivity, improved code quality, and cost-effectiveness. However, there is a lack of studies investigating how specific refactoring techniques affect reusability. Therefore, this study aims to explore the effects of code refactoring on software reusability. This study was conducted in four stages: selecting refactoring techniques, selecting a large dataset, selecting software metrics to measure the reusability, and applying the refactoring techniques. The five most common refactoring techniques (Extract Interface, Encapsulate Field, Extract Class, Inline Class, and Inline Method) used in practices were selected and applied in the jEdit dataset. The refactoring techniques were carried out 338 times through five experiments in the jEdit dataset. The finding reveals that Extract Interface, Encapsulate Field, and Extract Class significantly improve the code reusability while Inline Class and Inline Method impair it. Developers and software architects can use this knowledge to make well-informed judgments about refactoring techniques and develop more robust and reusable software systems.

关键词： Productivity Industries Codes Software quality Software systems Maintenance Software measurement

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