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Harnessing LLMs for Medical Insights:NER Extraction from Summarized Medical Text 15

Harnessing LLMs for Medical Insights:NER Extraction from Sum...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sasikala, D. Sudarshan, R. Sivasathya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Pondicherry University School of Engineering & Technology Department of Computer Science Pondicherry India

ISBN: (纸本)9798350370249

The rapid growth of medical text presents a significant challenge for healthcare professionals to stay updated with current research and clinical guidelines. Effective text summarization is essential to facilitate access to knowledge, improve clinical decision-making, and ultimately enhance patient outcomes. This study aims to fine-tune and assess a medical text summarization system utilizing large language models (LLMs). The objective is to produce concise, coherent summaries of medical literature, prioritizing clinical relevance. Additionally, automated named entity recognition (NER) is employed to extract vital entities from the summaries generated by GPT3, T5, PEGASUS LLMs. These LLMs are trained on benchmark PubMed dataset which are evaluated under standard metrics such as ROUGE (1, 2, L) and BERTScore metrics. The model pipeline demonstrates summarization and NER performance over precision of 6 5% on medical text data. The resulting entity-annotated summaries provide condensed representations, enabling efficient information extraction and knowledge discovery. This automated approach accelerates biomedical research by large-scale medical text analysis. © 2024 IEEE.

关键词： GPT3 large language model medical text summarization Named Entity Recognition PEGASUS T5

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Facial Expression Recognition Based on TripletLoss and Attention Mechanism 28

Facial Expression Recognition Based on TripletLoss and Atten...

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28th International Conference on Automation and computing, ICAC 2023

作者： Gou, Rongqiang Gai, Qiuyan Xu, Zhijie Xu, Yuanping Zhang, Chaolong Jin, Jin He, Jia Shi, Yajing School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (纸本)9798350335859

The paper proposes a facial expression recognition method called Triplet-Loss Attention Network (TAN), which aims to address the problem of large intra-class and inter-class distances in facial expression recognition. The method uses a self-attention mechanism to calculate the weight of triplet expression samples, which helps in determining the influence of key regions on expression recognition. The method uses images with attention weights above a certain threshold as training samples to form new hard triplets. The distance between high-weight samples in the three sets of samples is calculated using the Mahalanobis distance formula, and the difference in distance between high-weight groups of triplets is calculated using the Maharaja Loss function. The Triplet Loss is mainly used as the loss function in TAN, and the model is jointly optimized using Triplet Loss, Mahalanobis Loss, and Cross Entropy Loss functions to improve the performance of the model in facial expression recognition. Experimental results show that TAN performs well in alleviating the intra-class and inter-class distance problem and has good robustness and generalization performance. On the RAF-DB and FERPlus datasets, TAN achieves recognition accuracies of 88.40% and 88.73%, respectively, which is 1.37% and 0.18% higher than the previous state-of-the-art methods. © 2023 IEEE.

关键词： Face recognition

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Innovative Image Processing Techniques for Automating Cosmetic Inspection 5th

Innovative Image Processing Techniques for Automating Cosmet...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Panwar, Govind Singh Murthy, D.H.R. Agarwal, Trapty Jaison, Feon Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Progressive photograph processing techniques were developed for automating the technique of beauty inspection. These techniques make use of the wealthy, unique image records of the inspected objects to hastily become aware of diverse product defects. Specifically, object features including coloration, texture, size, shape, and location are first extracted from the photograph, and every function is then evaluated to come across cosmetic defects. Following the detection technique, the extracted capabilities are used to at once alter the manufacturing technique, enabling faster production turnaround times and progressed product quality. The strategies are designed to make certain rapid and accurate product inspection and also provide a bendy adjustment of parameters for various types of cosmetic merchandise. With these techniques, producers are capable of reduce cosmetically based totally product defects and supply improved great merchandise in a shorter time frame. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inspection

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Enhancing Agricultural IoT with Predictive Analytics and Reinforcement Learning Models 2

Enhancing Agricultural IoT with Predictive Analytics and Rei...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Krishnan, R. Santhana Raj, J. Relin Francis Christopher, V. Bibin Nandini, N. Muthu, A. Essaki Puvanadevi, C. SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Ssveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Electronics and Communication Engineering Chennai India Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies School of Computing College of Engineering and Technology Kattankulathur Chennai India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India

ISBN: (纸本)9798350317558

This research explores the augmentation of Agricultural Internet of Things (IoT) systems through the integration of advanced predictive analytics and reinforcement learning models. A novel algorithm, termed "CropQL,"is proposed to optimize crop yield prediction and resource allocation in precision agriculture. CropQL amalgamates the strengths of the CropNet IoT model with Q-Learning for reinforcement learning and Random Forest for predictive analytics. Simulation analyses are conducted to evaluate the performance of CropQL against established algorithms, employing diverse metrics such as precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The proposed CropQL algorithm exhibits superior predictive accuracy, demonstrating a significant enhancement in crop yield forecasting when compared to baseline algorithms. Precision and recall metrics reveal the algorithm's efficacy in minimizing false positives and negatives, ensuring precision agriculture systems make informed decisions. F1 score indicates the balanced optimization of precision and recall, reinforcing the algorithm's robustness. AUC-ROC analysis further corroborates the superior discriminative power of CropQL in distinguishing between positive and negative instances. In comparison to existing algorithms, including AgriSensNet, PrecisionCropNet, and IoTFarmGuard, CropQL consistently outperforms in predictive accuracy and resource optimization. The simulation results underscore the efficacy of the proposed algorithm in enhancing the efficiency of agricultural IoT systems, providing a promising avenue for sustainable precision farming. © 2024 IEEE.

关键词： Agricultural IoT CropQL Precision Agriculture Predictive Analytics Reinforcement Learning Sustainable Farming

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A Study on the Relevance of Vegetable Commodities Based on Pearson-Apriori Algorithm 24

A Study on the Relevance of Vegetable Commodities Based on P...

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3rd International Symposium on Robotics, Artificial Intelligence and Information engineering, RAIIE 2024

作者： Jia, Sidan Mao, Rui Tang, Chenghao Lu, Zanyu Gao, Yuanhang School of Mathematics & Computing Science Guilin University of Electronic Technology Guilin541004 China School of Materials Science and Engineering Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9798400718311

In fresh produce superstores, replenishment decisions and pricing decisions for vegetable items are particularly important. This paper investigates a data-driven approach for vegetable items in automatic pricing and replenishment decisions. By analysing vegetable sales data, descriptive statistical methods and Pearson's correlation coefficient are first applied to explore the linear relationship between vegetable prices and sales. The application of Pearson's correlation coefficient reveals linear correlations between different vegetable commodities as well as between commodity categories, providing direct quantitative guidance on price adjustment and replenishment strategies. In order to explore the complex relationship between price, sales volume and other potential factors in depth, the Apriori association rule mining algorithm was further applied in this study. The combination of Pearson's correlation coefficient and the Apriori algorithm can effectively analyse and interpret correlations and patterns in vegetable sales data, thus providing a data-driven basis for automated pricing and replenishment decisions for vegetables. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Vegetables

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Towards High-throughput and Low-latency Billion-scale Vector Search via CPU/GPU Collaborative Filtering and Re-ranking 23

Towards High-throughput and Low-latency Billion-scale Vector...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Tian, Bing Liu, Haikun Tang, Yuhang Xiao, Shihai Duan, Zhuohui Liao, Xiaofei Jin, Hai Zhang, Xuecang Zhu, Junhua Zhang, Yu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781939133458

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None of modern ANNS systems can address these issues simultaneously. In this paper, we present FusionANNS, a high-throughput, low-latency, cost-efficient, and high-accuracy ANNS system for billion-scale datasets using SSDs and only one entry-level GPU. The key idea of FusionANNS lies in CPU/GPU collaborative filtering and re-ranking mechanisms, which significantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically, we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We implement FusionANNS and compare it with the state-of-the-art SSD-based ANNS system-SPANN and GPU-accelerated in-memory ANNS system-RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN;2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy. © 2025 FAST. All Rights Reserved.

关键词： Collaborative filtering

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Traction-Braking of an EV in Mountainous Terrain with Hybrid Energy Storage System 11

Traction-Braking of an EV in Mountainous Terrain with Hybrid...

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11th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2024

作者： Ansari, Md. Irshad Misra, Himanshu School of Computing and Electrical Engineering Indian Institute of Technology HP Mandi India

This paper discusses the advantages of upgrading the existing battery electric vehicle (BEV) with the proposed configuration of a hybrid energy storage system (HESS). A simple and low-cost configuration for a HESS in an electric vehicle (EV) is analyzed and presented for hilly-driving regions. The performance of the energy storage system (ESS) during hilly terrain traction/braking is enhanced by leveraging the electrical property of each source i.e. Li-ion battery and ultracapacitor (UC). The simulation results show that the UC acts as a source/sink for all the transients during acceleration/deceleration conditions in the hilly driving pattern. Further, the experimental results in this paper show the high electrical stress on the battery that needs to be taken care of during the vehicle traction/electrical braking. Also, there is higher energy recovery when UC is used along with a Li-ion battery in the simplified configuration. © 2024 IEEE.

关键词： EV IM Li-ion Battery PMSM Three-Wheeler Ultracapacitor VSI

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Game Theoretical Distributed Task Offloading for Tethered Aerial Vehicles Communication Network 4

Game Theoretical Distributed Task Offloading for Tethered Ae...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Yang, Zhaohui Huang, Fengling Su, Xuqi Zhou, Pingfang Guo, Fusen Jing, Yuhao School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai China School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia School of Engineering and Information Technology University of New South Wales Canberra Australia

ISBN: (纸本)9798350395914

This paper introduces a game-theoretic framework aimed at enhancing offloading decisions within a network of multiple tethered aerial vehicles (TAVs) employed as communication base stations and relay platforms. Given their advantageous low deployment costs and extensive coverage areas, TAVs represent an innovative solution for dynamic air-ground communication networks. We address the inherent challenges associated with the mobility of ground users and consider across multiple temporal intervals. We initially segment offloading decisions into discrete time slots and model them within a strategic game principle. Subsequently, we prove that this game constitutes an exact potential game and introduce a distributed algorithm based on best response iteration. This algorithm is designed for decentralized deployment across individual tethered aerial vehicles. Through simulation studies, we demonstrate that our proposed methodology enhances network throughput relative to conventional random offloading strategies. It advances the theoretical understanding of offloading in dynamic network environments and offers practical algorithms for real-world applications. © 2024 IEEE.

关键词： Antenna grounds

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Early Detection of Amyotrophic Lateral Sclerosis with Time Collection Evaluation 5th

Early Detection of Amyotrophic Lateral Sclerosis with Time C...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Mehar, Palavancha Tirumala Krishna Agarwal, Trapty Suneetha, K. Ahmad, Naseem School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India

ISBN: (纸本)9789819780426

This paper discusses the usage of time series analysis for the early detection of Amyotrophic Lateral Sclerosis (ALS). A time series approach was used to expand a set of rules that may detect the early stages of ALS via analyzing symptomatic changes in signs and symptoms consisting of muscle energy, taking walks potential, speech, swallowing, and different bodily motion. This technique become examined on a retrospective dataset, and proven if you want to come across changes in the circumstance of ALS in a well-timed way. The outcomes suggest that this technique has ability as a tool for early detection in ALS sufferers. The proposed algorithm ought to potentially be used to improve the present day standard of care and help lead to in advance analysis and higher scientific effects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Motion Potential Swallowing

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A simplified design solution for shear capacity of post-installed anchors encompassing all failure modes in EC2-4 (2018) 15

A simplified design solution for shear capacity of post-inst...

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15th International Conference on Concrete engineering and technology, (CONCET 2022

作者： Ng, L.T. Looi, D.T.W. Lee, J. Ng, A.L.Y. School of Engineering and Science Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak93350 Malaysia School of Engineering Faculty of Science Engineering and Technology Swinburne University of Technology Hawthorn MelbourneVIC3122 Australia

Post-installed anchors are commonly used for steel-to-concrete connections, attributable to ease of installation as drilling technology advances. Besides, they offer the advantage of minimizing misalignment issues during construction compared to cast-in anchors. The anchor design calculations in EN 1992-4:2018 can be daunting for practicing engineers, given its complexity and state-of-the-art compared to ETAG 001 (2013). In an earlier publication, the authors described a methodology for simplifying the complex empirical equations in EN 1992-4:2018 for designing the tensile capacity of post-installed anchors into a simplified design tool. This work is a sequel to the previous work to develop a simplified design tool for estimating the design shear capacity of post-installed anchors (mechanical and bonded anchors) encompassing all failure modes. The calculation was first automated using a spreadsheet for the characteristic shear resistance of anchors and was verified using Hilti PROFIS engineering software. The simplified design tool is summarized based on the lowest resistance of the anchor encompassing all failure modes. The design tool is demonstrated for an example of a 125 mm thick C30/37 concrete slab for commonly available anchor sizes of M8, M10, M12, M16, M20, and M24. © Published under licence by IOP Publishing Ltd.

关键词： Anchors

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