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The Large Language Models on Biomedical Data Analysis: A Survey

IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4486-4497页

作者： Lan, Wei Tang, Zhentao Liu, Mingyang Chen, Qingfeng Peng, Wei Chen, Yiping Phoebe Pan, Yi Guangxi University School of Computer Electronic and Information Nanning530004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518055 China

With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis, followed by a detailed analysis of LLM applications in genomics, proteomics, transcriptomics, radiomics, single-cell analysis, medical texts and drug discovery. Finally, the challenges of LLM in biomedical data analysis are discussed. In summary, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research. © 2013 IEEE.

关键词： Data consistency

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Deciphering urban traffic impacts on air quality by deep learning and emission inventory

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Journal of Environmental sciences 2023年第2期124卷 745-757页

作者： Wenjie Du Lianliang Chen Haoran Wang Ziyang Shan Zhengyang Zhou Wenwei Li Yang Wang School of Software Engineering University of Science and Technology of ChinaHefei 230026China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China Alibaba Inc. Hangzhou 310052China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China CAS Key Laboratory of Urban Pollutant Conversion Department of environmental science and EngineeringUniversity of Science and Technology of ChinaHefei 230026China USTC-CityU Joint Advanced Research Center Suzhou 215123China

Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future ***,combined with the large volume of real-time monitoring data,we propose a deep learning model,iDeepAir,to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air *** model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355μg/m^(3) to 12.283μg/m^(3) compared with other *** identifies the ranking of major factors,local meteorological conditions have become a nonnegligible ***-wise relevance propagation(LRP)is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_(2.5) concentration in various regions of ***,As the strict and effective industrial emission reduction measurements implementing in China,the contribution of urban traffic to PM_(2.5) formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03%in 2011 to 24.37% in 2017 in Shanghai,and the impact of traffic emissions would be ever-prominent in 2030 according to our *** also infer that the promotion of vehicular electrification would achieve further alleviation of PM_(2.5) about 8.45% by 2030 *** insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control,and eventually benefit people’s lives and high-quality sustainable developments of cities.

关键词： PM_(2.5)concentration forecast Traffic emissions Deep learning Attention mechanism New energy vehicles

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Dual-Buck Bipolar Buck-Boost AC-AC Converter With Reduced Semiconductor Devices

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IEEE Transactions on Industrial Electronics 2024年第9期71卷 10495-10511页

作者： Ahmed, Hafiz Furqan Chien, Howard Khan, Ashraf Ali Islam, Shirazul Akbar, Fazal Alzaabi, Omar National Sun Yat-sen University Department of Electrical Engineering Kaohsiung80424 Taiwan Memorial University of Newfoundland Faculty of Engineering And Applied Science Department of Electrical And Computer Engineering St. John'sNLA1B 3X9 Canada Qatar University Department of Electrical Engineering Doha2713 Qatar University of Edinburgh EdinburghEH9 3JL United Kingdom Khalifa University Department of Electrical Engineering And Computer Science Advanced Power And Energy Center Abu Dhabi127788 United Arab Emirates

Dual-buck (DB) structured ac-ac converters are becoming advanced due to their inherent protection from open- and short-circuit risks, and elimination of commutation issue. However, the existing DB ac-ac converters provide restricted bipolar voltage operations with a large semiconductor device count and underutilized inductors. This article proposed a new reduced switch DB ac-ac converter without commutation issue. The propose converter provides multiple buck-boost voltage operations with noninverting and inverting outputs, making it a flexible topology for various ac voltage applications. The propose converter exhibits the continuous input and output currents irrespective of operation mode, and performs well with nonunity power factor loads. The positive and negative voltage gains of the proposed converter can address the grid voltage sag and swell issues, respectively, making it right candidate for application as dynamic voltage restorer. A detailed comparative evaluation is carried out with existing DB converters, which validated the effectiveness of the proposed converter, including;reduced semiconductor device count and their power ratings, reduced inductor sizes with improved magnetic utilization, enhanced voltage compensation ranges, and better efficiency. A laboratory scale hardware prototype of the proposed converter is built and thoroughly tested to validate its operation. © 1982-2012 IEEE.

关键词： Semiconductor devices

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Diverse Hazy Image Synthesis via Coloring Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3703-3713页

作者： Zhang, Shengdong Zhang, Xiaoqin Wan, Shaohua Ren, Wenqi Zhao, Liping Zhao, Li Shen, Linlin Shaoxing University Department of Computer Science and Engineering Shaoxing312000 China Shenzhen United Aircraft Technology Company Ltd. Guangdong Shenzhen518129 China Wenzhou University College of Computer and Artificial Intelligence Wenzhou325035 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China Sun Yat-sen University School of Cyber Science and Technology Shenzhen518110 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Convolutional neural network (CNN)-based dehazing methods have achieved great success in single image dehazing. However, the absence of real-world haze image datasets hinders the deep development of single image dehazing. To address this issue, we propose a diverse hazy image synthesis method based on generative adversarial network (GAN) and matting. Specially, we train a GAN-based model that can transform a gray image into a hazy image. To boost the diversity of hazy images, we propose to simulate hazy images via image matting, which can fuse a real haze image with another image containing diverse objects. To evaluate the performance of dehazing methods, we propose two novel metrics: part-based peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM). Extensive experiments are conducted to show the effectiveness of the proposed model, dataset, and criteria. © 2020 IEEE.

关键词： Three dimensional displays

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Post-Annealing Optimization for Top-Gate Amorphous In-Ga-Zn-O Thin-Film Transistors with Atomic-Layer-Deposited Ultrathin AlOx Dielectric

Post-Annealing Optimization for Top-Gate Amorphous In-Ga-Zn-...

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International Symposium, Seminar, and Exhibition, Display Week 2024

作者： Li, Jiye Yang, Huan Li, Xiao Zhang, Yuqing Wang, Xinwei Lu, Lei Zhang, Shengdong School of Electronic and Computer Engineering Peking University Shenzhen China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong School of Advanced Materials Peking University Shenzhen China

The effects of post-annealing on amorphous InGaZnO (a-IGZO) TFTs with ultrathin atomic layer deposited (ALD) AlOx gate insulator (GI) were investigated. The non-annealed devices exhibited uncontrollable gate leakage current while postannealing in either oxygen (O2) or nitrogen (N2) exhibited noticeable improvements. Optimizing O2-annealed devices demonstrated the most optimal performance, including a low subthreshold swing of 61.6 mV/dec and a high on/off current ratio over 109. Capacitance-voltage and X-ray photoelectron spectroscopy analyses further revealed the underlying mechanism of defect elimination at the AlOx/a-IGZO interface. © 2024, John Wiley and Sons Inc. All rights reserved.

关键词： Thin film transistors

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Deep Learning and Quantum-Enabled Cloud Platform Approach on Optimized Crop Health Predictions 5th

Deep Learning and Quantum-Enabled Cloud Platform Approach on...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Thakur, Hardeo Kumar Sharma, Neetu Alsahlanee, Abbas Thajeel Rhaif Sahunthala, S. Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chembarambakkam India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This study is an innovative investigation into the field of plant disease detection and plant health prediction using state-of-the-art machine learning models and Quantum Support Vector Machines (QSVM) for feature extraction. A diverse dataset of 700 images, including a variety of plant species and disease states, served as the foundation for this study. The study evaluated the performance of four machine learning models: CNN (ConvNet), RNN (Recurrent), KNN (k-Nearest Neighbors), and SVM (Support Vector Machine) for accurately identifying and classifying plant diseases. Convolutional neural network (CNN) model unexpectedly prevailed, achieving a remarkable accuracy rate of 98.77%. Accuracy, recall, and F1 score of the model further demonstrated its propensity for disease diagnosis. QSVMs were used to extract intricate features from pictures of sick leaves in order to increase the models’ precision and prognostication ability. The findings have significant impact for agriculture. And the sophisticated toolkit they offer has provided an important step forward in proactive disease management for better crop health. Carrying out an early intervention for disease can result in higher crop output, lower agricultural losses, and improved global food security. The QSVM’s application of quantum computing principles also opens up fresh frontiers for creating machine learning applications in different industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT 30

Benchmarking Large Language Models for Persian: A Preliminar...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Abaskohi, Amirhossein Baruni, Sara Masoudi, Mostafa Abbasi, Nesa Babalou, Mohammad Hadi Edalat, Ali Kamahi, Sepehr Sani, Samin Mahdizadeh Naghavian, Nikoo Namazifard, Danial Sadeghi, Pouya Yaghoobzadeh, Yadollah Computer Science Department University of British Columbia Vancouver Canada School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Tehran Institute for Advanced Studies Khatam University Tehran Iran

ISBN: (纸本)9782493814104

This paper explores the efficacy of large language models (LLMs) for Persian. While ChatGPT and consequent LLMs have shown remarkable performance in English, their efficiency for more low-resource languages remains an open question. We present the first comprehensive benchmarking study of LLMs across diverse Persian language tasks. Our primary focus is on GPT-3.5-turbo, but we also include GPT-4 and OpenChat-3.5 to provide a more holistic evaluation. Our assessment encompasses a diverse set of tasks categorized into classic, reasoning, and knowledge-based domains. To enable a thorough comparison, we evaluate LLMs against existing task-specific fine-tuned models. Given the limited availability of Persian datasets for reasoning tasks, we introduce two new benchmarks: one based on elementary school math questions and another derived from the entrance exams for 7th and 10th grades. Our findings reveal that while LLMs, especially GPT-4, excel in tasks requiring reasoning abilities and a broad understanding of general knowledge, they often lag behind smaller pretrained models fine-tuned specifically for particular tasks. Additionally, we observe improved performance when test sets are translated to English before inputting them into GPT-3.5. These results highlight the significant potential for enhancing LLM performance in the Persian language. This is particularly noteworthy due to the unique attributes of Persian, including its distinct alphabet and writing styles. We have made our codes, prompts, and data available here: https://***/Ipouyall/Benchmarking_ChatGPT_for_Persian. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Benchmarking

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Recyclability and Self-Healing of Dynamic Cross-Linked Polyimide with Mechanical/Electrical Damage

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Energy & Environmental Materials 2023年第1期6卷 401-407页

作者： Baoquan Wan Ming-Sheng Zheng Xing Yang Xiaodi Dong Yuchao Li Yiu-Wing Mai George Chen Jun-Wei Zha School of Chemistry and Biological Engineering University of Science and Technology BeijingBeijing 100083China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing 100083China Shunde Graduate School of University of Science and Technology Beijing Foshan 528300China School of Materials Science and Engineering Liaocheng UniversityLiaocheng 252059China Centre for Advanced Materials Technology(CAMT) School of AerospaceMechanical and Mechatronic EngineeringThe University of SydneySydney New South Wales 2006Australia Department of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK

Recyclability and self-healing are two most critical factors in developing sustainable polymers to deal with environmental pollution and resource *** this work,a dynamic cross-linked polyimide insulation film with full closed-loop recyclability is successfully prepared,which also possesses good self-healing ability after being mechanical/electrical damaged depending on the Schiff base dynamic covalent *** recycled and self-healed polyimide film still maintain its good tensile strength(r t)>60 MPa with Young’s modulus(E)>4 GPa,high thermal stability with glass transition temperature(T g)>220℃,and outstanding insulation property with breakdown strength(E 0)>358 kV mm^(-1),making it a very promising low energy consumption and high temperature resistant insulation *** strategy of using Schiff base dynamic covalent bonds for reversible repairing the structure of high T g polyimides promotes the wider application of such sustainable and recyclable material in the field of electrical power and micro-electronics.

关键词： electrical damage polyimide recyclability self-healing

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Hybrid Modulation for a SHB-FB Converter Employed in An Integrated On-board Charger and Auxiliary Power Module

Hybrid Modulation for a SHB-FB Converter Employed in An Inte...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Liang, Ziwei Bijukchhe, Rajib Zhu, Liyan Bai, Hua Cebanov, Evgheni Samila, Myron Forouzesh, Mojtaba Phillips, Headley University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States Virginia Tech The Bradley Department of Electrical and Computer Engineering BlacksburgVA United States Magna Powertrain Inc. Advanced Engineering & e-Propulsion VaughanON Canada

ISBN: (纸本)9798350376067

This paper presents a highly integrated EV charger that offers multiple charging modes, including a three-phase input 11 kW on-board charger (OBC) and a 2.6 kW auxiliary power module (APM). The proposed multi-level, three-port topology achieves magnetic and topological integration simultaneously and accommodates the three-phase (3P) 480VAC, 800V DC bus using 650V GaN HEMTs. A mathematical model of the ZVS boundary of the proposed Stacked half bridge (SHB)- full-bridge (FB) converter is built. One hybrid modulation method is further proposed to achieve zero voltage switching (ZVS) for a wide power and voltage range. A liquid-cooled prototype is built to validate the proposed integration method and hybrid modulation. Experimental results verified the OBC and APM modes of the proposed topology and the effectiveness of the hybrid modulation in improving the light-load efficiency by achieving ZVS. © 2024 IEEE.

关键词： Gallium nitride

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Gastrointestinal Disease Classification And Analysis Using GI-Net Model

Gastrointestinal Disease Classification And Analysis Using G...

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Malviya, Animesh Dutta, Malay Kishore Department of Computer Science and Engineering Center for Advanced Studies Lucknow India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Detecting gastrointestinal illnesses accurately is crucial to early cancer detection and treatment. In spite of this, manual analysis is time-consuming, requiring the assistance of a gastrointestinal. A multi-class classification framework for screening gastrointestinal diseases is proposed that is efficient and robust. A neural network known as gastrointestinal GI-Net was developed to extract features that could differentiate between normal and diseased images taken by endoscopy device. In order to achieve the most optimal classification network, a variety of optimization techniques are used. For the classification network to be more effective, the framework can handle the challenges present in the dataset. It is 88% accurate in diagnosing using unseen endoscopy images. In comparison with other deep learning networks, the developed architecture is highly effective. Compared to other networks, in limited computation environments, the proposed network is likely to perform better. © 2022 IEEE.

关键词： Endoscopy

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