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Kalman-based Adaptive Moment Estimation Optimisation Algorithm to Enhance GPT in LLMs for Medical Sentiment Analysis of Patient Health-related Feedback

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Chen, Xingchi Ma, Wenxin Li, Dazhou Zhu, Fa Routray, Sidheswar Guduri, Manisha Margala, Martin The Pengcheng Laboratory Shenzhen518000 China The College of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110142 China The College of Information Science and Technology College of Artificial Intelligence Nanjing Forestry University Nanjing China The Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India The School of Computing and Informatics University of Louisiana LafayetteLA70503 United States

The progress in Natural language Processing (NLP) using large language Models (LLMs) has greatly improved medical sentiment analysis of patient feedback extraction from health-related question and answer. However, using LLMs to analyze such data often requires significant training data and computational resources, resulting in considerable increases in training costs and durations, which is one of the primary issues in applying LLMs to real-world healthcare scenarios. To tackle these challenges, a novel optimization algorithm named KAdamEnGPT4LLM, based on Kalman filters and Adaptive Moment Estimation, is proposed to enhance training efficiency and reduce training costs of LLMs for analyzing patient feedback sentiment. Furthermore, the optimization algorithm KAdam-EnGPT4LLM is employed in training the LLM model GPT4ALL for medical sentiment analysis, resulting in the development of GPT4ALLMediSentAly-KAdam, which leds to faster convergence and more stable training specifically for medical questions and answer in the context of healthcare. The results show that our GPT4ALLMediSentAly-KAdam with the optimization algorithm KAdamEnGPT4LLM achieved better performance that include the best Accuracy, Recall, F1-score, and Runtime for both datasets, outperforming traditional fine-tuned LLMs such as the classic GPT4ALL, Ada, Babbage, Curie, and Duvinci. © 2013 IEEE.

关键词： Adaptive filters

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Interplay of Remote Sensing and Artificial Intelligence in Open Agricultural land Monitoring

Interplay of Remote Sensing and Artificial Intelligence in O...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Anya Eliza Y. Almalbis Alexandra Brianne B. Gochian Raphael C. Murillo Alexandra Dominique M. Uy Ronnie Concepcion Immanuel Jose Valencia Argel Bandala Department of Science Technology Engineering and Mathematics De La Salle University – Integrated School Manila Philippines Department of Manufacturing Engineering and Management De La Salle University Manila Philippines Department of Electronics and Computer Engineering De La Salle University Manila Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

As a prominent global player in rice production, the Philippines has consistently upheld its commitment to ensuring food security and meeting the demands of its population. This was highly evident in the year 2021 when the nation achieved a milestone by yielding a harvest of 19.96 million metric tons of rice. However, due to its geographical location in Southeast Asia and its influence on global warming, the country is highly susceptible to the devastating effects of tropical cyclones. Climate change, particularly the rise in global temperatures, makes oceans heat up more. This leads to an increase in the frequency and intensity of weather events that pose a significant threat to the agricultural sector and profoundly impact the livelihood of farmers who heavily rely on crops. Several studies collectively aimed to assess the impact of crop losses on agricultural fields, particularly areas prone to natural disasters. These studies utilized diverse methodologies that comprehensively evaluated the impact of these disasters on crop fields and the economic losses in the area, therefore providing significant insights into environmental factors and agricultural productivity in farming areas. The methodologies employed in these studies focused on improving crop prediction through data analysis techniques. Machine learning approaches like Support Vector Machines and Decision Tree algorithms were utilized in enhancing the efficiency of crop management. Data collection was gathered from diverse sources like government websites. While technology-driven approaches like data logging, satellite imagery, and machine learning were used to locate and map crop production stages.

关键词： Support vector machines Productivity Tropical cyclones Disasters Sociology Crops Machine learning

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Solid State Electrolyte to Enable Structural Supercapacitors

Solid State Electrolyte to Enable Structural Supercapacitors

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IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS)

作者： Nandu Koripally Lulu Yao Jason D. Azoulay Tse Nga Ng Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA Materials Science Engineering Program University of California San Diego La Jolla CA USA Chemisty Department Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350383263

ISBN: (纸本)9798350383270

Supercapacitors are known for longer cycle life and faster charging rate compared to batteries. However, the energy density of supercapacitors requires improvement to expand their application space. To raise the energy density of structural supercapacitors, this work demonstrates a low resistance and mechanically strong solid-state electrolyte.

关键词： Resistance Supercapacitors Electrolytes Sensor systems Sensors Batteries

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X-ray simulations with gVXR as a useful tool for education, data analysis, set-up of CT scans, and scanner development 15

X-ray simulations with gVXR as a useful tool for education, ...

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15th SPIE Conference on Developments in X-Ray Tomography

作者： Vidal, Franck P. Afshari, Shaghayegh Ahmed, Sharif Atkins, Carolyn Béchet, Éric Bellot, Alberto Corbí Bosse, Stefan Chahid, Younes Chou, Cheng-Ying Culver, Robert Dixon, Lewis Friemann, Johan Garbout, Amin Hatton, Clémentine Henry, Audrey Leblanc, Christophe Leonardi, Alberto Létang, Jean Michel Lipscomb, Harry Manchester, Tristan Meere, Bas Middleburgh, Simon Mitchell, Iwan Perera, Liam Puig, Martí Tugwell-Allsup, Jenna Scientific Computing Ada Lovelace Centre Science Technology Facilities Council United Kingdom School of Computer Science & Engineering Bangor University United Kingdom Department of Biomechatronics Engineering National Taiwan University Taiwan DIAD Beamline Diamond Light Source United Kingdom UK Astronomy Technology Centre Royal Observatory Edinburgh United Kingdom Département d’Aérospatiale et Mécanique Université de Liège Belgium Escuela Superior de Ingeniería y Tecnología Universidad Internacional de La Rioja Spain Department of Computer Science University of Koblenz Koblenz Germany Department of Mechanical Engineering University of Siegen Siegen Germany The Manufacturing Technology Centre United Kingdom Department of Industrial and Materials Science Chalmers University of Technology Sweden Henry Royce Institute Henry Moseley X-Ray Imaging Facility Department of Materials The University of Manchester United Kingdom Scalian DS Rennes France INSA-Lyon Université Claude Bernard Lyon 1 CNRS Inserm CREATIS UMR 5220 U1294 Lyon France Department of Mechanical Engineering Eindhoven University of Technology Netherlands Department of Engineering Science University of Oxford United Kingdom Ysbyty Gwynedd United Kingdom

ISBN: (纸本)9781510679641

gVirtualXray (gVXR) is an open-source framework that relies on the Beer-lambert law to simulate X-ray images in real time on a graphics processor unit (GPU) using triangular meshes. A wide range of programming languages is supported (C/C++, Python, R, Ruby, Tcl, C#, Java, and GNU Octave). Simulations generated with gVXR have been benchmarked with clinically realistic phantoms (i.e. complex structures and materials) using Monte Carlo (MC) simulations, real radiographs and real digitally reconstructed radiographs (DRRs), and X-ray computed tomography (CT). It has been used in a wide range of applications, including real-time medical simulators, proposing a new densitometric radiographic modality in clinical imaging, studying noise removal techniques in fluoroscopy, teaching particle physics and X-ray imaging to undergraduate students in engineering, and XCT to masters students, predicting image quality and artifacts in material science, etc. gVXR has also been used to produce a high number of realistic simulated images in optimization problems and to train machine learning algorithms. This paper presents applications of gVXR related to XCT. © 2024 SPIE.

关键词： computerized tomography

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Advanced wind turbine blade inspection with hyperspectral imaging and 3D convolutional neural networks for damage detection

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Energy and AI 2024年第2期16卷 528-537页

作者： Patrick Rizk Frederic Rizk Sasan Sattarpanah Karganroudi Adrian Ilinca Rafic Younes Jihan Khoder Department of Mechanical Engineering Universitédu QuébecàTrois-RivièresDrummondvilleQCCanada Centre national intégrédu manufacturier intelligent(CNIMI) Universitédu QuébecàTrois-RivièresDrummondvilleQCCanada Department of Mathematics Computer Science and EngineeringUniversitédu QuébecàRimouski300 allée des UrsulinesRimouskiQCG5L3A1Canada The Center for Advanced Computer Studies University of Louisiana at LafayetteLafayetteLAUSA T3E Industrial Research Group Mechanical Engineering DepartmentÉcole de Technologie Supérieure1100 Notre-Dame St WMontréalQC H3C 1K3Canada Faculty of Engineering Third BranchLebanese UniversityHadathBeirutLebanon LISV Laboratory University of Versailles Saint-Quentin-en-Yvelines10-12 Avenue de l'Europe78140VélizyFrance

In the context of global efforts to mitigate climate change by pursuing sustainable energy sources, wind energy has emerged as a critical contributor. However, the wind energy industry faces substantial challenges in maintaining and preserving the integrity of wind turbine blades. Timely and accurate detection and classification of blade faults, encompassing issues such as cracks, erosion, and ice buildup, are imperative to uphold wind turbines' ongoing efficiency and safety. This study introduces an inventive approach that amalgamates hyperspectral imaging and 3D Convolutional Neural Networks (CNNs) to augment the precision and efficiency of wind turbine blade fault detection and classification. Hyperspectral imaging is harnessed to capture comprehensive spectral information from blade surfaces, facilitating exact fault identification. The process is streamlined through Incremental Principal Component Analysis (IPCA), reducing data dimensions while maintaining integrity. The 3D CNN model demonstrates remarkable performance, achieving high accuracy in detecting all fault categories in full-band hyperspectral images. The model retains high accuracy even with dimensionality reduction to 20 spectral bands. The reduced processing time of the 20-band image enhances the practicality of real-world applications, thereby reducing downtime and maintenance expenditures. This research represents a significant advancement in wind turbine blade inspection, contributing to the sustainability and dependability of wind energy systems and furthering the cause of a cleaner and more sustainable energy future as part of the broader fight against climate change.

关键词： blade thereby turbine

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large-Scale Distributed Multidisciplinary Design Optimization of the NASA Lift-Plus-Cruise Air Taxi Concept

Large-Scale Distributed Multidisciplinary Design Optimizatio...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： van Schie, Sebastiaan P. C. Ruh, Marius L. Fletcher, Andrew Warner, Michael Sperry, Mark Scotzniovsky, Luca Orndorff, Nicholas C. Xiang, Ru Yan, Jiayao Zhao, Han Krokowski, Joshua J. Chen, J.S. Sarojini, Darshan Gill, Hyunjune Lee, Seongkyu Tagg, Andrew Anderson, Ryan Green, Eric Joseph, Cibin Ning, Andrew Cheng, Zeyu Cao, Zhi Mi, Chunting Guibert, Alexandre T. R. Cronk, Ashley Kim, H. Alicia Meng, Y. Shirley Silva, Christopher Hwang, John T. University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Department of Mechanical and Aerospace Engineering University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Department of Structural Engineering University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Aerospace and Ocean Engineering Department Virginia Tech 1600 Innovation Dr BlacksburgVA24060 United States Department of Mechanical and Aerospace Engineering University of California Davis 1 Shields Avenue DavisCA95616 United States Mechanical Engineering Brigham Young University ProvoUT84602 United States Department of Electrical & Computer Engineering San Diego State University 5500 Campanile Dr San Diego92182 United States Structural Engineering Department University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Materials Science & Engineering Program University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Pritzker School of Molecular Engineering The University of Chicago ChicagoIL60607 United States NASA Ames Research Center Moffett FieldCA94035 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

large-scale gradient-based Multidisciplinary Design Optimization (MDO) can aid in the exploration of high-dimensional design spaces for novel air vehicle concepts, thereby leading to more efficient and economic designs. This paper builds on past works where we demonstrated large-scale physics-based MDO capabilities and applied these to NASA’s lift-plus-cruise electric air taxi concept. We extend this comprehensive mid-fidelity system-level optimization problem with high(er)-fidelity subsystem-level optimizations of various aircraft systems and mission phases, with the aim of further enhancing the accuracy and scope of the aforementioned system-level optimization problem: 1) Power minimization during the transition mission phase;2) Electrical powertrain topology optimization;3) Cell chemistry modeling and thermomechanical battery pack topology optimization;4) Shell-based coupled aero-elastic wing structure optimization. An Analytical Target Cascading-like distributed MDO architecture allows us to couple the system- and subsystem-level optimizations in order to arrive at a consistent and feasible design. We find that the system-level design is most heavily impacted by the reduced battery pack-level energy densities that stem from power peaks in the mission profile. These power peaks seem to result from the inefficient lift rotor blade designs that are needed to satisfy noise constraints. We draw conclusions based on trends in our results and give recommendations for future MDO studies of electrical air taxi vehicle concepts. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Shape optimization

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PEBERS: Practical Ethereum Blockchain based Efficient Ride Hailing Service

PEBERS: Practical Ethereum Blockchain based Efficient Ride H...

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2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020

作者： Kudva, Sowmya Norderhaug, Renat Badsha, Shahriar Sengupta, Shamik Kayes, A.S.M. La Trobe University Department of Computer Science and Information Technology Melbourne Australia

ISBN: (纸本)9781728148212

Although the current ride hailing services have revolutionized the transportation industry in today's world, they are extremely centralized. Central authority has all the control and maintains data about the users. Such platforms raise concerns about the service policies and data reliability. In case the central server faces data tampering or ransomware attacks, all the records of data is either lost or compromised. Blockchain has gained exceptional recognition while addressing aforementioned issues because of its decentralized data auditability, anonymity and immutability. Research on blockchain technology explores the possibility of implementing decentralized application in ride hailing service. Blockchain based ride hailing architecture gives us the opportunity to deviate from centralized platforms to decentralized ones. In this paper we propose our system PEBERS: Practical Ethereum Blockchain based Efficient Ride Hailing Service, in which we demonstrate how decentralized system based on consortium blockchain can be developed to keep track of ride data. In this context we explore smart contracts to build and deploy functionalities such as create ride, auto deposit transfer, cancel and complete ride methods of our decentralized ride hailing application. Our experiments show the driver smart contracts proposed in our system consume less Gas and hence prove that its an efficient system than many other existing systems with respect to the expenses of passengers and profitability of drivers. This is an effort towards providing smarter transportation for a society moving towards smart city concept. © 2020 IEEE.

关键词： Blockchain

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Towards hybrid approach based SVM and Radiomics features for COVID-19 classification and segmentation

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International Journal of Hybrid Intelligent Systems 2024年第PRE-PRESS期Pre-press卷 1-20页

作者： Azizi, Ridha Sakly, Houneida Wajdi, Abdallah Ahmed Hmida, Alaa Eddinne Ben Bouhlel, Med Salim Research Lab: Smart systems for Engineering and E-health based on Technologies of Image and Telecomunications (SETIT) ISBS Sfax University Tunisia Center for Research on Microelectronics and Nanotechnology (CRMN) Sousse Technopole Sahloul Sousse Tunisia RIADI Laboratory ENSI Manouba University Campus Universitaire de la Manouba La Manouba Tunisia Department IT Higher Institute of Computer Science and Mathematics of Monastir Tunisia Department IT Higher Institute of Computer Science and Mathematics of Monastir Tunisia

In the battle against the COVID-19 pneumonia outbreak, which is brought on by the coronavirus strain SARS-Cov-2, radiological chest exams, such as chest X-rays, are crucial. In order to understand the unique radiographic characteristics of COVID-19, this research looks into classification models to distinguish chest X-ray images based on Radiomics features. This study is performed with datasets composed of 136 segmented chest X-rays, which were used to train and test the categorization algorithms. First and second-order statistical texture characteristics were extracted from the right (R), left (L), superior, middle, and bottom lung zones for each lung side using the Pyradiomics collection. Data was divided into training (80%) and test (20%) groups for feature selection. After assessing the respective feature significance and confirmation accuracy, the most pertinent Radiomics features were chosen. A model of lung segmentation based grey level pixels was used to evaluate support vector machines (SVM) as possible classifiers (AUC = 83.7%). Our research reveals a preference for the upper lung zone and a preponderance of Radiomics feature selection in the right lung. Our future research will concentrate on COVID-19 categorization and segmentation for more precise forecast using a hybrid method based on SVM and Radiogenomics features.

关键词： COVID-19 radiomics pyradiomics classifiers segmentation SVM radiogenomics

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FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data

arXiv

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arXiv 2024年

作者： Jing, Shusen Yu, Anlan Zhang, Shuai Zhang, Songyang Department of Radiation Oncology University of California San FranciscoCA United States Department of Electrical and Computer Engineering Lehigh University BethlehemPA United States Department of Data Science New Jersey Institute of Technology NewarkNJ United States Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA United States

Recent efforts have been made to integrate self-supervised learning (SSL) with the framework of federated learning (FL). One unique challenge of federated self-supervised learning (FedSSL) is that the global objective of FedSSL usually does not equal the weighted sum of local SSL objectives. Consequently, conventional approaches, such as federated averaging (FedAvg), fail to precisely minimize the FedSSL global objective, often resulting in suboptimal performance, especially when data is non-i.i.d.. To fill this gap, we propose a provable FedSSL algorithm, named FedSC, based on the spectral contrastive objective. In FedSC, clients share correlation matrices of data representations in addition to model weights periodically, which enables inter-client contrast of data samples in addition to intra-client contrast and contraction, resulting in improved quality of data representations. Differential privacy (DP) protection is deployed to control the additional privacy leakage on local datasets when correlation matrices are shared. We also provide theoretical analysis on the convergence and extra privacy leakage. The experimental results validate the effectiveness of our proposed algorithm. © 2024, CC BY.

关键词： Supervised learning

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Tensor Levenberg-Marquardt Algorithm for Multi-Relational Traffic Prediction

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IEEE Transactions on Vehicular Technology 2023年第9期72卷 11275-11290页

作者： Chang, Shih Yu Wu, Hsiao-Chun Kuan, Yen-Cheng Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan International College of Semiconductor Technology Hsinchu30010 Taiwan Communications Research Centre Canada OttawaONK2H 8S2 Canada

Traffic prediction serves as a crucial role in intelligent transportation systems for applications including routing planning and traffic control. With the advances of sensor technologies and Internet of Things (IoTs), the exploitation of traffic data motivates us to reformulate the traffic-state prediction problem based on the big-data characteristics. In order to infer multifarious traffic information, the standard one/two-relational traffic data in the vector/matrix form needs to be expanded to multi-relational traffic data in an arbitrary tensor form. However, none of the existing approaches is capable of performing traffic prediction by characterizing and tracking the inherent multi-relational nonlinear dynamics which are often encountered in the realistic time-series analysis. Furthermore, there hardly exist convergence studies on convergence condition and convergence speed pertinent to the existing traffic-prediction methods. In order to combat the aforementioned challenges, we propose a new tensor Levenberg-Marquadt algorithm (TLMA) to conduct the multi-relational nonlinear regression for traffic prediction involving tensorized data. Meanwhile, we also derive the lower-bound of the number of iterations required to make the error-norm below a specified tolerance for global convergence. Besides, we derive the conditions for our proposed TLMA to converge to the optimal solution. The convergence rates with respect to different nonlinear cost functions have been derived theoretically. The computational- and memory-complexities of our proposed new TLMA are studied as well. Finally, numerical experiments are conducted to evaluate the traffic prediction performance of our proposed new TLMA over real traffic data in comparison with other existing multi-relational prediction methods. © 1967-2012 IEEE.

关键词： Tensors

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